Digital Methods

Video thumbnail (Frame 0) Video thumbnail (Frame 13531) Video thumbnail (Frame 15196) Video thumbnail (Frame 19320) Video thumbnail (Frame 22364) Video thumbnail (Frame 23900) Video thumbnail (Frame 26540) Video thumbnail (Frame 29201) Video thumbnail (Frame 31147) Video thumbnail (Frame 33621) Video thumbnail (Frame 35342) Video thumbnail (Frame 38189) Video thumbnail (Frame 48589) Video thumbnail (Frame 54560) Video thumbnail (Frame 56328) Video thumbnail (Frame 59833) Video thumbnail (Frame 62223) Video thumbnail (Frame 63769) Video thumbnail (Frame 65361) Video thumbnail (Frame 67452) Video thumbnail (Frame 70622) Video thumbnail (Frame 73855) Video thumbnail (Frame 75656) Video thumbnail (Frame 78672) Video thumbnail (Frame 80382) Video thumbnail (Frame 82915) Video thumbnail (Frame 85935) Video thumbnail (Frame 91107) Video thumbnail (Frame 95714) Video thumbnail (Frame 98577) Video thumbnail (Frame 100214) Video thumbnail (Frame 101746) Video thumbnail (Frame 103555) Video thumbnail (Frame 107744) Video thumbnail (Frame 109565) Video thumbnail (Frame 111180) Video thumbnail (Frame 113588)
Video in TIB AV-Portal: Digital Methods

Formal Metadata

Digital Methods
Objects & Approaches
Alternative Title
Social Media Data Analysis: Wikipedia, Twitter, Facebook, Instagram, YouTube and Deep Vernacular Web
Title of Series
Part Number
Number of Parts
No Open Access License:
German copyright law applies. This film may be used for your own use but it may not be distributed via the internet or passed on to external parties.
Release Date
Production Year
Production Place
Dubrovnik, Croatia
State observer Context awareness Multiplication sign Modal logic Source code Archaeological field survey Cyberspace Coma Berenices Mereology Facebook Virtual reality Hypermedia Different (Kate Ryan album) Analogy Query language Cybersex World Wide Web Consortium Building Digitizing Digital divide Bit Virtualization Digital signal Price index Flow separation Type theory Wave Digital photography Process (computing) Internetworking Website Quicksort Row (database) Spacetime Geometry Classical physics Observational study Transformation (genetics) Average Graph coloring Number Pi Internetworking Term (mathematics) Computing platform Condition number Projective plane Basis <Mathematik> Incidence algebra Cyberspace Human migration Word Maize Visualization (computer graphics) Query language Computing platform
Classical physics State of matter Multiplication sign Source code Bit rate Computer font Number Neuroinformatik Twitter Sound effect Frequency Computational physics Latent heat Telecommunication Term (mathematics) Different (Kate Ryan album) Hypermedia Googol Query language World Wide Web Consortium Source code World Wide Web Consortium Computer chess Observational study Building Digitizing Projective plane State of matter Sound effect Digital signal Price index Incidence algebra Measurement Word Googol Internetworking Hypermedia Query language Search engine (computing) Order (biology) Quicksort Geometry
Scale (map) Group action Observational study Digitizing Plotter Computer-generated imagery Projective plane Analytic set Digital signal Mathematical analysis Peer-to-peer Medical imaging Category of being Word Software Different (Kate Ryan album) Software Matrix (mathematics) Cuboid Spacetime Quicksort Quantum Form (programming) Spacetime
Scale (map) Information management Observational study Observational study Multiplication sign Digitizing Computer-generated imagery Morley's categoricity theorem Mathematical analysis Parameter (computer programming) Continuous function Substitute good Frequency Mathematics Hypermedia Software Spacetime Analytic continuation Form (programming) Descriptive statistics Formal grammar
Topology Real number Multiplication sign Mathematical analysis Formal language Medical imaging Mathematics Software Core dump File viewer Smoothing Form (programming) Google Bücher Theory Analytic set Limit (category theory) Category of being Number Googol Logic Mixed reality Right angle Quicksort Freeware Reading (process) Geometry
Covering space Web page Metric system Link (knot theory) Social software Information Link (knot theory) Graph (mathematics) Digitizing Constructor (object-oriented programming) 1 (number) Hyperlink Computer network Digital signal Mathematical analysis Bookmark (World Wide Web) Exterior algebra Different (Kate Ryan album) Personal digital assistant Software File viewer Quicksort Ranking Metric system
Classical physics Web crawler Metric system Link (knot theory) Social software Link (knot theory) State of matter Projective plane Source code Mathematical analysis Hyperlink Computer network Mathematical analysis Price index Bookmark (World Wide Web) Measurement Mathematics Software Hypermedia Software Object (grammar) Quicksort Metric system
Touchscreen Metric system Ferry Corsten Multiplication sign View (database) Digital signal Hyperlink Sphere Neuroinformatik Timestamp Uniform resource locator Facebook Order (biology) Hypermedia Object (grammar) Query language Row (database) Website Extension (kinesiology) Personal identification number Source code World Wide Web Consortium Observational study Link (knot theory) View (database) Building Digitizing Point (geometry) Shared memory Sampling (statistics) Virtualization Digital signal Price index Web crawler Bookmark (World Wide Web) Measurement Twitter Formal language Hierarchy Arithmetic mean Exterior algebra Hexagon Sample (statistics) Internetworking Facebook Order (biology) Quicksort Physical system Spacetime Point (geometry) Asynchronous Transfer Mode Observational study Motion capture Electronic mailing list Mathematical analysis Content (media) Coprocessor Twitter Product (business) Number Frequency Goodness of fit Read-only memory Term (mathematics) Software Computer hardware Operating system Representation (politics) Contrast (vision) Computing platform Condition number World Wide Web Consortium Information Characteristic polynomial Archaeological field survey Code Computer network Coprocessor System call Web browser JSON Word Event horizon Software Personal digital assistant Search engine (computing) Universe (mathematics) Revision control Computing platform Object (grammar) YouTube Traffic reporting
Multiplication sign Parameter (computer programming) Function (mathematics) Dreiecksnetz Mereology Bookmark (World Wide Web) Formal language Medical imaging Different (Kate Ryan album) Semiconductor memory Pairwise comparison Boss Corporation Observational study Dreizehn Moment (mathematics) Bilderkennung Formal language Element (mathematics) Federation of Bosnia and Herzegovina Proof theory Frequency Arrow of time Quicksort Web page Point (geometry) Table (information) Observational study Variety (linguistics) Computer-generated imagery Simultaneous localization and mapping Similarity (geometry) Student's t-test Content (media) Event horizon Revision control Latent heat Term (mathematics) Energy level Operations research Execution unit Projective plane Mathematical analysis Number Software Alphabet (computer science) Personal digital assistant Revision control Object (grammar) Force Address space
Point (geometry) Observational study Information Multiplication sign Computer-generated imagery Bit Content (media) System call Element (mathematics) Medical imaging Uniform resource locator Word Software Read-only memory Personal digital assistant Term (mathematics) Pressure volume diagram Order (biology) Revision control Energy level Pairwise comparison Metropolitan area network Electric current Address space
Point (geometry) Presentation of a group Observational study Metric system Social software Multiplication sign Source code Set (mathematics) Digital signal Mathematical analysis Expert system Generic programming System call Twitter Twitter Event horizon Term (mathematics) Spacetime Reading (process)
Presentation of a group Word Observational study Metric system Multiplication sign Telecommunication Remote procedure call Quicksort Generic programming Twitter Number Twitter
Mathematics Blog CNN Hypermedia Planning Quicksort Event horizon Form (programming) Twitter Surface of revolution Twitter
Observational study Social software Virtual machine Mathematical analysis Mathematical analysis Event horizon Twitter Virtual machine Twitter Sound effect Sign (mathematics) Order (biology) Event horizon Resource allocation Internetworking Hypermedia Different (Kate Ryan album) Website Quicksort Remote procedure call Website Curve fitting
Observational study Multiplication sign 40 (number) Set (mathematics) Mathematical analysis Expert system Event horizon Twitter Uniform resource locator Frequency Voting Robotics Term (mathematics) Hierarchy Query language Spacetime Information Endliche Modelltheorie Website Computer-assisted translation Observational study Key (cryptography) Analytic set Measurement Twitter Word Resource allocation Internetworking Facebook Event horizon Frequency Software Network topology Video game Quicksort Boiling point Square number Spacetime
Email Observational study Execution unit Source code 1 (number) Set (mathematics) Mathematical analysis Twitter Frequency Web service Different (Kate Ryan album) Term (mathematics) Spacetime Maize Office suite Text editor Hill differential equation Relief Form (programming) Patch (Unix) Software developer Content (media) Expert system Extreme programming Twitter Type theory Event horizon Order (biology) Right angle Quicksort Spacetime
Observational study Multiplication sign Execution unit Electronic mailing list Mathematical analysis Core dump Mathematical analysis Extreme programming Subgroup Twitter Twitter Medical imaging Software Core dump Query language Right angle Figurate number Quicksort System identification Writing
Multiplication sign Mathematical analysis Core dump Bit Mathematical analysis Complete metric space Electronic mailing list Line (geometry) Twitter Bookmark (World Wide Web) Twitter Word Event horizon Visualization (computer graphics) Oval Core dump Touch typing Spacetime Right angle Quicksort Figurate number YouTube Curvature
Observational study Real number Set (mathematics) Mereology Twitter Formal language Facebook Frequency Hypermedia Series (mathematics) Extension (kinesiology) Computing platform Presentation of a group Observational study Demo (music) Web page Morley's categoricity theorem Generic programming Extreme programming Lattice (order) Group action Evolute Entire function Virtual machine User profile Word Facebook Network topology Right angle Quicksort Figurate number Object (grammar)
NP-hard Trajectory Wechselseitige Information Group action Social software Observational study Multiplication sign Simultaneous localization and mapping Limit (category theory) Virtual LAN Nominal number Bookmark (World Wide Web) Facebook Medical imaging Profil (magazine) Different (Kate Ryan album) Term (mathematics) Hypermedia Endliche Modelltheorie Extension (kinesiology) Dean number Scalable Coherent Interface Source code Execution unit View (database) Projective plane Instance (computer science) Term (mathematics) Demoscene Information privacy Similarity (geometry) User profile Sign (mathematics) Internet forum Universe (mathematics) Convex hull Object (grammar) Quicksort Musical ensemble Figurate number Metric system Family Identity management Booting
Web page Point (geometry) Group action Social software Observational study Multiplication sign System administrator .NET Framework Denial-of-service attack Mathematical analysis Content (media) Disk read-and-write head Facebook Medical imaging Goodness of fit Term (mathematics) Profil (magazine) Ring (mathematics) Software Spacetime Extension (kinesiology) Presentation of a group Data dictionary Computer chess Observational study Information Sine Web page Content (media) Electronic mailing list Extreme programming Group action Surface of revolution Virtual machine User profile Facebook Software Personal digital assistant Right angle Musical ensemble Quicksort Matrix (mathematics)
Web page Multiplication sign Computer-generated imagery Execution unit Set (mathematics) Mathematical analysis Content (media) Data transmission Product (business) Facebook Medical imaging Googol Software Query language Software testing Extension (kinesiology) Macro (computer science) Maß <Mathematik> Observational study Building Web page Shared memory Content (media) Computer network Digital signal Data transmission Arithmetic mean Facebook Internetworking Software Quicksort Spacetime
Web page Sign (mathematics) Facebook Hypermedia Social software Facebook Information Source code Universe (mathematics) Form (programming) Gradient descent
Trajectory Observational study Observational study Transformation (genetics) Wage labour Projective plane Analytic set Bit Instance (computer science) Digital photography Coefficient of determination Causality Drum memory Right angle Object (grammar) Quicksort Gamma function Family Spacetime
Dependent and independent variables Observational study Term (mathematics) Multiplication sign Wage labour Query language Computing platform Mereology Quicksort Physical system Number
Classical physics Point (geometry) Observational study Robot Source code Mathematical analysis Power (physics) Twitter Hypermedia Dilution (equation) Right angle Website Position operator Computing platform YouTube Pay television Hoax Structural load Projective plane Data storage device Content (media) Sound effect Computer network Flow separation Symbol table Hierarchy Software Personal digital assistant Right angle Quicksort Videoconferencing YouTube
Email Asynchronous Transfer Mode Pay television Observational study Password Content (media) Bit rate World Wide Web Consortium Source code Pay television Gateway (telecommunications) Theory of relativity Forcing (mathematics) Computer network Core dump Term (mathematics) Open set Tube (container) Inclusion map Software Web service Normed vector space Videoconferencing Form (programming) Reading (process)
Trajectory Execution unit Observational study Cross-platform Multiplication sign Electronic mailing list Extreme programming Coma Berenices Flow separation Human migration Inclusion map Facebook Word Propagator Extreme programming Arrow of time Normal (geometry) Musical ensemble Extension (kinesiology) Computing platform Reading (process) Spacetime
World Wide Web Consortium Observational study Observational study Transformation (genetics) Multiplication sign Projective plane Control flow Mathematical analysis Digital signal Rule of inference Twitter Latent heat Facebook Event horizon Read-only memory Object (grammar) Order (biology) Bus (computing) Right angle Object (grammar) Quicksort Series (mathematics) YouTube Spacetime
my name is richard rogers i'm a professor of new media and digital culture at the university of amsterdam some coming to from i'm sort of and what i want to do this morning so are you have me for quite a long time so if you have me first for a session on the study of. the social media and then subsequently for a session on what is an event so that one will be a little bit shorter and this will be all take the full all take a full time i want to talk about the expertise of that we have an amsterdam. and also what you will be confronted with when you come to the summer school which is either next summer the summer there after. and the expertise there is something we call digital methods so what i want to do his own to tell you a little bit about that and how we study the social media and more generally so first woman to do is i'm going to situate of the study of digital methods historically a sort of talk about where. where we are historically in terms of research with the internet and with web data specifically. the woman to do is i'm going to situate digital methods a piste melodically that is to say how does digital methods of these the way i understand it differ or are is similar with approaches in the digital humanities as well as in the as well as individual sociology or are you the social sciences. generally and then finally i want to show you have quite practically how digital methods are done so i just want to kick off by saying that the digital methods. and that the idea of using the web as a data source is fairly recent so it when studying the internet or when starting the web so historically oftentimes retreated the web businesses are a sort of space apart something quite distinctive and this particular idea. of of cyberspace is still with us so the word cyberspace is not used that often but it's still used at these two contexts cybersecurity and cyberwar that's a cyber space so as a separate row but cyberspace as. otherwise in some ways dated also as an approach or as a metaphor an approach to the study of of the internet so it's no longer thought of as a as a space apart i think that that changed at least for a lot of people were the arrival of the digital ethnography furs and about ninety ninety nine two thousand. people like christine heinen are famous book virtuous no graffiti as well as daniel slater and on miller they in some ways did something quite interesting. they were part of a wave of scholars who went to the cybercafes when to people's houses interviewed them observe them of how they actually use the internet so this sort of over the shoulder type methods and they in some ways ushered in a. number of really important concepts and also approaches they coined the term virtual methods and virtual methods were the importation of social science method importation and transformation or even migration of social scientific method on to the web. and what we did somewhere around two thousand and seven two thousand eight is we declared the end of the virtual. and and coined the notion of digital methods as distinctive from virtual methods to digital methods would would use the methods of the medium so not import methods from the social sciences participant observation its etc and talk about things like the digital divide and though. rather of the the this was the study of media method so one of the methods that have been written for the medium and are in some sense of the medium there's another particular really interesting idea and this is something that is still something that we use and this is the question of the baseline. so previously any time you use the web data there would always be the question of well ok you made some findings with the web but now we need to ground the offline. with digital methods are introduced the idea as the of the online as the baseline. the idea out of or the question under which conditions can use the online as the baseline as the sight of your findings and also the site of the grounding they're off. most recently and i won't talk too much about this today others than the term introduced the post digital and it's very interesting the effort to turn and of itself and what the post digital argues is that is that there's nothing no longer distinctive about the digital. so so if you use the term digital methods like i do you actually need to use that because we no longer need to make this distinction between analog and digital or between online and offline if or even even intimated that there is such a distinction necessary. that's one way this notion is used the other way the pows digital is used is it was it in the sense of post a.p.i. research for nowadays as you know i'm a talk about this of a little while facebook specifically but other platforms less so have. cut off access to data from the pages a.p.i. of facebook officially under if you guys probably experienced this is well on for the september i was the end and what has come and its place is the social science one project which i'm also old apart. of as a critic. from two later but anyway this is the post digital were of this is the second idea of what the pows digital is that is locked platforms and the need for post digital method or post a.p.i. research. ok so. i want to get to an example of a digital with his right away this is quite a classic. this was published in the new york times i think in two thousand and ten this is using data from all recipes dot com and the data here are the queries of on this recipe website for thanksgiving recipes so this is a sort of america. and feast every year and the day before thanksgiving people started searching the web for recipes and the dark of the color the the the higher the incidence of queries for in this one is three potato pie the sea were sweet potato pies been cleared for four. or proportionally more so than elsewhere. and so here is macaroni and cheese also in the side of sweet potato wary of the midwest or corn casserole this is like tradition of the corn belt and the u.s. green beans on the west coast turkey brian very northern so what you did here. but quite quite conventionally almost is a kind of geography of taste and job a fee of taste of the basis of of where data on the basis of of queries in almost his doctor now what's interesting about it. is to ask yourself the question of how would i don't know how would have done this differently. how else could have created these visualizations so he could think about surveys you could think about. you know i mean the other thing you could do is say well how can i ground this in the online so can i use of in-store graham photos of of of geo geotagged instead graham pictures of meals on thanksgiving itself so you see here that the idea of the of using. web data and as an indication of what is happening all and then grounding in the online is quite a durable.
this one you can see the outline of the states but this is then a specificity measure so which of which recipes were most specific required for this came out a few years later. i think you all will have heard of if not read about if not cited a number of these sources which around two thousand and nine when a week coin this idea of digital methods there was also the coinage of the notion of computational social. science so computational social science of is a term that a. i don't know if it was first coined in this particular science article but by david was there and others as being. the similar idea of using the web as a data source and this data source would be then as indicators of what is happening in society duncan was a few years before that also talked about this particular a turn now something happened quite drastically a.
all of years later they called into question in some ways. big data are the computational turn and this was google flu trends google flu trends of course is a very famous project. which used the flu and flu related queries and the geo coordinates from those queries in order to gain a sense of where the incidence of flu was so could we use search engine queries to try. back. flu and this was not the only project of its kind there are better few others the national institute of health in the us actually read a contest in two thousand and fourteen writer of this time asking for those different kinds of web techniques where you could provide a pretty low. logical indicators but the interesting thing here was that suddenly in this particular period google flu trends was found to have bend over estimating the incidence of flu and this is the this is the biggest problem that we all conference when using web data. so are we measuring something in the wild. or are we measuring something in media and what's the difference. so what was found was that that's the over or of reporting of these particular of queries or the over incidents there have probably came from the fact that people would watch t v hearing that it's clue flu season and then start query. to find out about the flu not that they had it themselves. so this is a sort of classic immediate effect. so this is the question that one is always asking whether as a sort of the the big data flagship project with a big data or where data are measuring societal trends are measuring media effects. ok so what i want to talk about now is just more logically what word out to situate digital methods and want to talk about it with respect to not not all approaches in the humanities and the social sciences but just a couple that i want to flag. as well known approaches and then situated your methods of their in i'm going to do it like this.
and. what you're looking at this matrix is a two by two is the difference between the digital and the digitized or the natively digital and the digitized and i'm talking about that in two ways talking about natively digital. data and digitised data and natively digital method and digitised method and then given were particularly approaches how a particular approaches and the humanities and the social sciences how they operate so to speak what they use ives i've. place them in these boxes and they don't want to go through unexplainable but while have done that i'm going to start with the digital humanities. and in particular on cultural analytics cultural analytics is an interesting a sort of a small movement almost no it was coined by love monitor which come some years ago i think two thousand and ten or two thousand and nine. and of course it has at least it shares the key word with our own project and a living but otherwise not that much so what manage and others have gone subsequently its there's now a journal of cultural analytics. which i think started last year and. what it is is this idea that you can how does its idea about how to study so the digitized art and literature and images so the money which magician.
so this this sort of stuff. his image awards. so what money which and others have proposed are and also created so this is this particular software his image plot. an image plot is as software that's has been regularly updated have been used recently all but nevertheless what does a group's images. by their formal properties. and it is an art historical approach so when you begin to have a sort of describe artworks materially you probably reminded of labels at art museums or oil on canvas but that's what this goes a step. for other where they're looking at. hughes such aeration etc so what none of which the argues is what i want to study here is a style space for this style space on provide you with a sense of different either peer.
kids or changes in a particular artists worked like mark rothko or others that have been specific and else's on those.
or also over time and what is what is interesting about monitors arguments is this here and why he considers big data interesting big date is often times critiqued. but in the digital humanities and particular or in media studies are what this is very interesting what will monitor argues he argues that big data at the big a big hitter approach has two things going for to speak won his one no longer needs to period dies. rather what you see is continuous change so you see a gradual changes will have to period rising have said the modern and the postmodern and the premodern. the other one is you no longer have to categorize so it in art history or in history. let's keep with our history. what what one is doing theoretically often is your period arising and categorize so what's a month which proposes is know what what we have with big data and when we visualize it like this over time we have continuous change and continuous description.
this is maybe one of his more well known works not that long ago although now that the instrument a p i no longer co-operates with any of us this with no not really be possible but this is this is the work self the city itself the city dot net so what mom of a church. and colleagues did was queried hashtag sell free and the geo coordinates of five cities real. tokyo moscow los angeles and another one. and then are you can see the top took the form of properties of thirty five hundred that's the marks that your limit or these was of the time and three and thirty five hundred images and made findings about sort of city sentiment or mood so he found real. so was quite jolly and moscow was quite grim rights that as you can imagine the it for its cultural analytics i want i want to move not a cultural mix this is another home. i mean. it's in the i would cost five individual humanities because what does is it again uses digitised data but this time digitized books so it relies on the google and graham your. and it in fact realize most heavily on the english language won the and graham your haven't that has the the but the better data so to speak but there are also other languages as well.
what's interesting about cultural mix. is that it does interesting work for linguists like saw her for years and other kind of cultural story of the disease is more broadly where one could look at stormont a logical change over time and ask a lot of questions that one. arguably would not have been able to ask without a sort of cotton core distant reading approach we can talk about the critics have just really if you like as well but one of the more interesting findings for example of interest in the digital humanities is that celebrity to be. being famous has shortened over time. so this the the duration of a celebrity or the idea of a celebrity is far short of overtime for example.
also look at the history of different cultural constructs of and which ones have faded and which ones have bent on the ascendancy looking what largely into of cultural intellectual turns.
get. so. of what i've basically showed you are two schools of thought and digital humanities and how these rely most significantly on a digitised data so so stand or digitized paintings or. it works magazine covers and digitised books and there are also using methods that arguably or also digitize so bettors from art history than a year in port in two of them into the digital what i want to do now is i want to talk about on. a couple of approaches in the individual sociology or bordering on information science that views natively digital data but have digitized method and these are a classic won which is whether metrics and a sort of knew. we were won which is or newish not the newry longer alternatives are so whether metrics classically and and and in some ways this goes all the way back to page rank but to both but also to other ideas of our of hyperlinks the value. hyperlinks value of hyperlinks also could be seen and the paths that it creates. and in any case what metrics is something that looks at links.
as indicators of reputation and of course if you get links from higher more highly reputed as sources less reputed sources then your reputation rises as of this particular idea this is that this is software that i build the issue crawler but there's also. other software. all of it that you can do hyperlink analysis and this and still to the state was a new there's a new project out of sensible media lab they're called hive which is hyperlink a sort of cure a song or collection making a piece of software but nevertheless what we're doing here. is we are. using kind of classic scientists metrics or blue metrics thinking about. like almost like citation analysis and then applying that to the native the digital so you have reputation measures like from science some objects applied then subsequently to to links.
the other one out which is of more recent vintage of his old metrics you probably if you've seen the doughnut.
next to a particular publication online so this is an alternative means of metro finding your citation scores so instead of looking at academic citations or other publications you look at how. often that's been mentioned in social media what's interesting so this again use is made of the digital data with again is so traditional scientific scientific methods are digitized methods. what's interesting about this is at the at first glance you could be quite cynical about this and there have been a lot of studies to ask the extent to which by mike fell on others extent to which the ultimate tricks provides a good indicator of academic reputation if you use the weather. science as a baseline for example and its found that that's actually not the case so all the tricks is very alternative so it's in fact its own universe of measurements so to speak however what's interesting about it is that it does capture. also what else have year and another sort of major private the publishers are buying up and that that is. academic referencing or academic of bookmarking software so if so it also measures that the extent to which publications of pure in these in these places as well. so now if you if you haven't guessed by now.
we're going to talk about this other space some of my colleagues are also very interested in that space that empty space their mother talk about that today but i'm going to just talk about. the study of natively digital method and it of the digital data and i was just mention what i mean by the of the digital i don't think as i need to say so much about that for this particular room but what i'm talking about with the native. from his like like this piece of hardware that this is the actual mike adapter ride so it's not the emulated once it's have written for the for the processor for the operating system so the idea of the new digital is written for the medium now this does not mean so i don't mean native and after. portugal sense i also don't need it mean it as something that is exclusive that is pure on either because one can take a lot of bunch of methods from wherever information was computers etc and adapt them out to to the web. but this is your you're actually writing the of a specifically for for the medium if you will and so this is what i mean and then i. the contrast those with the methods that are specifically the from especially the social sciences of the humanities but specifically migrated and what's interesting about its of the debate between what was once i mean i call these digitize nothing but they were once called virtual methods. the debate about the extent to which one should use virtual methods versus digital methods. is that a lot of these a lot of the methods that are used from the social sciences with the web. have issues with things that they didn't have issues with previously for example knowing the population and what becomes a good sample if you know the work of gary king at harvard. he has created the data capturing or data extraction device called a crimson hexagon you've heard of there's probably some of you i'm so what crimson exit on does is it takes a sort of sampling procedure. whereby you say i i for this particular period of time if i want tweets from generally want to to december thirty first two thousand eighteen with a hashtag climate change of the key word climate change what this piece of software does give you ten thousand tweets. now is that representative. we have no idea or least there's there's there's the we could mean there are ways of acquiring twitter of course to find out what you know how many they have what the population is but that's not the particular. idea behind this this sampling approaches just ten thousand so the idea of getting something representative or knowing how good your example is these things have some some issues so what i put forward is and instead of have constantly trying to import. so scientific methods the medium rather to actually think about those methods which are so in some ways a native. and a lot of these methods rely on things that i think that we're now increasingly take for granted that is the idea that ok online there are hyperlinks online there are likes their shares their dates times there are such as such so when you think of those objects anything so vocal one. can i do with these so what kind of productive work of research going to do with with these need of the digital objects. what i also proposes it is that we can learn from the dominant of online devices and their own methods so we can learn from a number of of of computational methods that are classically used online and then the the idea of in the eye. in. in digital methods to sociology at such a cultural and social research more broadly is to think about how to repurpose these methods so you know what i showed you before and repurpose thing is the key term here so why should you before about the search engine queers people are quick. during but then how do we use those corners to make other findings about what's happening in society and culture more more generally. people are lying king how can we use those to find out what's happening this sighting culture more generally and then finally asking the question was how do i ground these findings and when and under which conditions is appropriate to ground them in the online and so this is the digital methods gambit it's very very different. from the digitized ok so what i want to do is i want to show your some of them and have no home. i'm going to talk may me about these are so promise to talk about social media. and so are all going to talk a little bit about as a wikipedia twitter facebook is to go on you tube and all also talk about read it and four chan and want to talk and have to have included as but i can also talk about telegram. so if you take this this general philosophy that that i put to you how would you then approach these platforms and these objects of study and how they're all how would that be different from how others have traditional approach so we can pin year wikipedia. but oftentimes has been approached on its own terms as an encyclopedia and the queen and the questions have been as well how accurate is how and how much of an encyclopedia is its encyclopedia ness so speak. so that arguably would be a digitized research questions of the woods or your however if you think about it from a digital methods point of view so how'd how can we think about the objects that are available there and how can we repurpose them in order to make findings about what's happening in society and culture more. really.
so this is the first project of its kind where we started. the difference between. but the same article across a variety of languages. so we're not starting with the pews neutrality here were in fact studying its procure was of course particularly what is specific to the pages the articles of in a particular culture he and i. just put up as example for you quickly so you could see that if you look at wikipedia and were not the only people to discover the so but then been two or three other projects many pedia only pedia of that have done the same of and so were to see your this is should bring it's a business is a case study that i did. others to demur in a bosnian student nine because it's a very significant. the moment in history historical of that if you will for the bosnians it's also very significant historical event for the dutch because the dutch un battalion was protecting should run it so when the bosnian serbs came in and massacred. i was isn't a question if you look at the different wikipedia articles of your notice that they have different names. and then at the time of the research and sure exactly what is right now also different victim it's so the dutch of who are their tender ground down. and others tender turned around op and. so if you think about it. i'm studying wikipedia not as encyclopedia well for starters you but rather for its as a cultural reference and look at the various objects that are available you notice that there are specific objects of ill specific with a p.o. objects that you can look out and so what we did is his. we build software to very simple software and then others. colleagues of ours to build more complex software to study a cultural difference and the same wikipedia articles. so i just want to show you have a couple of these outputs so one own is to extract the references so we could be the articles have of course or referenced and so we built the simple to all of the part of a suite of two. falls to just grab the references and then compare them drop them and what you notice is that across the various wikipedia articles they're not referencing the same fate is on a host level even so on a page level on the you can see that so you can explain cultural difference in terms of very distinctive referencing. my personal favorite is the image of and and are now also saw for so so this is cross how. across cultural image analysis so what we're looking at here is which images are used as kind of argument objects for particular history a version of events per wikipedia article which of which are present which are absent and then there's also the question of whether one. can the with competition all techniques not just use difference to get exact but but get some sort of for the ideas of of similarity and difference what i want to just mention two years the significance of this kind of work like for cultural analysis.
and that is. to look very specifically which images are present which are obscene so hours what a point you ought to this one this is a significant this is a have a great start of a thirteen year old boss to bore. this gravestone is in the bosnian. but it's not in the dutch. it's not have the serve so why is that well i mean that could be a variety of reasons but what's important to know is that this is proof of that there was genocide thirteen year old go. and this is a sort of significant for the bosnians the idea also that it's a it's genocide. or it's a massacre is a very very different terms of very very different consequences so you can see this particular sort of those caught politics of memory playing out very specifically on these wikipedia page as wikipedia articles.
so i only showed you just comparing references and images but of course as those on other things to be compared and and and what's and can be quite significant. we also looked into a man maybe it's just mention is really briefly.
what the one thing about wikipedia it's a little annoying and terms of its data is that it provides the location of anonymous letters but doesn't provide location information of registers as the one thing it's and one piece of missing data and wikipedia. but nevertheless you can get an indication of of the places of adults and so were these and it's coming from and then when you look to the and then you can look at the over time so we have built the software which can look at the overtime and what we're really interesting is to see how. the prob counter-intuitively at least two which the peons that over time the pork marriage would just make this into appear question not just talk about this case over time due articles tender improve and become more neutral order the become more particular and parochial. so what we found and i'll tell you in an actor maybe have lunch because i told this finding to the king of the netherlands. to what boy. that's america at a later but what he thought was oh all over time you know once all the facts and on then then you know all the articles will become the same i was like him you know i think it's the other way around. this is for this particular case so they become farther apart. and one way of looking into that is of course into the history of of of whose editing and the level of let's say multicultural editing it wasn't for particular point in time. on the serbian article there were no on serbs and on the bosnian article the word on bosnian editing for various short course of time until it became a little bit more let's call it tribal.
on. our twitter twitter of twitter's of course toward or two.
there's interesting read what i find interesting about twitter is how it has transformed itself all over the past sort of decade. call it whatever thirteen years from what it was at the beginning to what is now in terms of data source kinds of questions a kind of research people are doing with it so in the beginning and just show you conceive have it.
here so this is the original twitter stretch this is jack dorsey co-founder. and this is in two thousand and six up until recently this was on jack dorsey is a flicker manage this particular historical document so you can see the kind of history of of the point in time and twitter was was thought of the was a domain hack status. but us of status this idea this is ultimately not what it became but own. i. it had it was directed or starter for khan son francisco and urbanites right and the the to default settings that you had was in bed or going to the park would see its some sense of who we were thinking about here as the users of them. so this was the the you what are you doing twitter from which many found to be quite banal.
it was often times referred to as the what i had for lunch twitter. oh oh oh that's what you have for lunch interesting and. but but scholars who took it seriously said well actually what's happening here is you should study is substantially just started fact technically so the idea of fatty communication so what we're doing his words were not sharing and in follow now we're connecting for connecting and your father. calling people so intimately to the their number of notions but for the time and the stupid for today. remote in remote intimacy ambient friend following these are the sort of ideas about the value of twitter now what happened was quite radical a.
on a quite suddenly out around two thousand and nine or in two thousand and fact was that they went over night from a what i had for lunch and medium to revolutionize.
now of course people were following events on twitter and one to talk about there's also the second thing of the before lunch about what is an event in media people were following the australian wildfires the the spectacular landing of a ploy. plane in the hudson river of remember this were no one was injured as and this was first known on twitter to. and then the journalists following twitter with then a twitter was the sort of anticipatory news medium so but so it was happening to extend however it turned into this revolutionary medium this event following medium where the the tagline changed.
from what are you doing to what's happening and this is the this is the sort of two thousand and nine of kind of.
twitter so what we did is we just thought about what kind of methods of can we develop have to pay for this sort of remote event twitterers it as a as the site for a moat event analysis so what we did very simply. it is we tried to do a couple of techniques were returned twitter into a storytelling machine so this idea that that twitter can tell you what's happening on the ground and in social media through some simple techniques.
so this is of. an artwork this isn't a vivid have a lot of different. the galleries around the world it's it's the iran election. hashtag the what we did is really took the are around election hashtag. and we have so this is just a single hastert and then we took the most retreated read most for true tweets per day and put them in chronological order as as opposed to reverse chronological order and then compressed it i'm so as to get a jurist of the what happen.
and during a particular set of events he also demarcated of the duration of the event through activity measures. and we were able to boil down the iran election crisis of into of these five tweets. on twitter is also often times considered as a kind of issue space for professionals.
generally speaking when making a tree collection or i'm a lot of times people will use a collection of hashtags and key words were query the the streaming a pure a set up a tree collection beginning from that day moving. ford what we've found is that you can create a more robust when collection if you first where the search a p.r. i have with your key words and hashtags and then and then do do a coal ash diagnosis or hashtag frequency and else's and get the other hashtags and key words that you've missed. first and then add the to regional sets and then kwiry again so this will get you a on arguably more of robots three collection in terms of our study that collection as an issue space so when you look at twitter analytical software we've created one. all the mit can have a twitter caption and else's tool or to cut a deal mighty cat there are others you'll notice that there are normally you know life twenty five thirty forty models and look at anything more what i do here i want to analyze this three collection as an issue space this is. the particular technique that we've developed were just a very simple recipe of looking at hashtags uncoerced tags has times oftentimes embedded social issues or and or embedded events so you can see the sort of hierarchy of the concerns of a particular issue space.
you can also look at the out mentions in a similar way a macho you grab some purpose first lists so you can understand the space of so this is this is the study of dominant voice which voices are getting mention the most not the ones we're talking the most the most vocal but the ones that are much more men mention the most.
and finally.
which in order to as a form of content in office for rich are which your els are are mentioned or in fact hosts to this is the difference between hillary supporters and some supporters in the twenty sixteen election period you see very very distinctive. a set of sources being reference by the in fact if you look at the so this is now as of common finding a new york i bet a blur others have made it but the sort of radicalisation of the right if you look at the types of sources that are being referenced year and they're quite extreme of to the right by the by the trumps holes where is the hill. there is a porters they're quite mainstream. i want to show you a technique that so on. forbidden by twitter but i think is. is a worthwhile academic approach and that's a said segmenting audience so if you look at the developer terms of service on twitter it's as do not segment an audience. and it's directed largely government owes surveillance this particular idea but i think for research purposes it is completely ok if you want to study a social movement of the tetra and and this is an interesting question which we can come back to about the. the larger issue of breaking terms of service. so while we did is we took we we we were working with other british home office the country extremism a unit of the british home office that's the ministry of internal affairs of the uk if and they were the experts who provided.
a list of the core all to write figures on twitter at the time they've now subscale been banned the core all tried for years and so we did was we didn't at mention analysis so there's two ways of doing this.
do the second way that the first where was our do or mention his you take a core of our of a movement. and then you say who mentions them. so you clearing twitter for at meant for account names you don't normally do that are normally do that if you quit for for had accountings then you see who mentions that you also see images of them recently so you get that sort of fresh sort of support network or you can or you can kwiry. some who they mention so this is what we did here so this is the sort of core of the altar rights that this is defined by the country extreme isn't units and then who they mention and what's what so scary but fairly well known hours is these it these are who are the eight.
that's that the core mention role trouble. so many that this isn't the so this is this is that this is the audience segmentation technique which actually reveals quite significant findings and i'm so who did i mention and so then you can sort of build this is because this network of of of the all. it's right if you will now what is a troubling for some probably here is that your usernames here and so should they be blacked out. it's interesting of twitter of i don't know if you studied the don't russian ira a. twitter account collection or the rainy and the so-called rainy and disinformation collection of twitter released. last october or so. in it they have the user names of those who have fewer than five thousand followers centering twitter has defined i mean this is this is quite significant twitter has to find a public figure as having five thousand followers have more. so you could use that as your you could simply use that if you follow the medium or you could come up with other definitions of a public figure and and block the rest are and i just want to say when studying twitter that there are a lot of people or i think this is also very interesting to do do this sort of this. these this analysis of leaders have here this is one of my favorite pieces of data journalism an r.v. seen it and the new york times the five hundred sixty seven people places and things donald trump has in south it.
this is a quite a nice day extraction access to and visualization and. so i was talking with these these the the these these days journalist there were in touch with has recently and they want to do another word for an hour says of the relationship between lots of the all tried and trump via via his tweets and they're asking how would we do that and so when you see this in the near times. not exactly but a there there are planning a lot of day their journalism in the run up to the to the upcoming elections along these lines and also the relationship between trump and the and extreme right so if you see it you can think of this a stalk so here on this is in. it's very journalistic so who is insulted and then of of the ties such that you could do a little bit more analytically so this is something that we did for the the dutch populist a leader.
and this is taking a twee collection the entire tree collection of of the populace leader or another public figure and then just staying through a series of techniques the extent of the extreme this so you can think through how you would do it could do it. we did it through. categorizing tweets as being part of a kind of scientology or definition of extremists or that were used from this london based think tank demos which said that the new right is different from the old right or the old daughter sort of. like the old nazis in the sense of their use. some language of the last so they're antiglobalization for example to the new ride have all tried would have so so in this particular announces we took a set of given. a given definition of what what the all trite is and then extracted the tweets automatically car categorize them and then and then looked at them to see with the good of the automatic categorization made sense and then made some conclusions about real hair builders six. and to a cheese. all try populists leader and we found that these he isn't he isn't just more complicated. ok on facebook i want to talk about. i mean so.
i didn't do we should add a fourth period hear the richest september fourth two thousand and eighteen and forward i think i didn't do that yet. but i should the but i want to have i want to just talk about facebook studies where it's ben and and and also words going so. i think of facebook as facebook as an object of study has evolved i think more significantly than any other social media platform i know that's twitter one for you know the what i have for lunch meeting tour of revolutionary medium to a two hour kind of generic datasets i know that that's quite a. evolution but i think facebook says is even more stark. so it started as an the object of study one one one one.
the scene of the movie the social network you recognize this guy was one of the one of the co-founders of he's also the guy who have revolutionised the music industry to do you remember what he founded and yet so in in the social network. he plays is really funny role so he he says to mark zuckerberg you know the facebook the facebook you are all that's really lame it's going to facebook so this is his or her these kind of well known for for the sort of brand imaging facebook but no nonetheless and. early days when it was still lower facebook and then subsequently they're just there after the objects of study was this so the profile and the friends and as always with the scare quotations extent to which the virtual real it cetera. and i'm this this work. see heard of this year if this work on taste and ties of thinking about facebook as predominately something which you could use to study.
the extent to which you one's friends tend to have the same interests are the same of profiles it set or not this changed quite significant so there is something the quite significantly happened in two thousand and ninety thousand dollars a year. two thousand it would begin to happen so there is a previous study at harvard where the data were anonymised so so the hard research is doing taste and ties research. did precisely that so do friends have gradually have the same profile interests. and what michael them are at the university of. was constant were walking what he did was he successfully quite easily d. anonymised data and he started for the first time arguably of ushering in what has now become a quite a significant ethics turn and social media research so are. you get started here but in its it's picked up a lot more steam recently houses cambridge analytica. but but this is where arguably it started so i'm around that time people are still studying. profiles and interests and so one of the techniques that we developed i think i was a. there's actually wikipedia entry for this term of this isn't a redevelopment capos demographics its its it's not that different from socio metrics if you will which is so which is the beginning in some ways of the of social network analysis and some sense but in other senses has a different trajectory. but this is the idea of studying. compatibility. of of interests amongst public figures friends and so this was the first instance that we did this this was the this is when obama was was against john mccain this was the two thousand and eight presidential elections and looking. yet their friends and the friends interests and looking for come compatibility so this isn't some sense to the research questions to ask your concern extent of the culture wars hard and so do obama's friends share the same interests as as mccain's friends do the democrats and that have the same media. tastes and speak and what's interesting here for example is that is that the democrats the obama people they were they watch things like the daily show and last was the republicans their favorite shows our family guy project runway america's next top model desk. housewives there is a very very specific difference between the media consumption of one group or the other the other way i mean you can also profile a particular the compatibility of one interest with other interests as well we did the.
ability of islamic christianity in terms of what do they share the same music case if you have a if you gave his love for christianity as one of your general interest.
but i think. some mean what happened with the a.p.i. to point our particular was that all the profile information even for used to be there was a point where you could get the profile of information of your friends you get your friends and all their profile information that you could do a little miniatures. my working hours that ended with a p.r. to point all and arguably also with the same time came the rise of the study very very different facebook and as a study of pages and that is going on until.
through two weeks ago on and arguably this is one of the turning points my been the most famous facebook page and history this is there really are all covered so you'd have to just revolution of two thousand and eleven. and this has been a page that has been analyzed quite often. and around this time we developed software called net of his and techniques of which are called into like page else's and network content and i'll so pages skin like other pages. and this is the of this of extreme right in europe. this is a who which pages like with other pages so give it a seed list of so you can still do this work of it's become quite difficult of the end of the pages of coverage you could still could conceivably do this work of what was interesting at the time. was it should also pull up the names of the ministry us now that our your facebook with would cringe this saw this image here right now but this is these are the the administrators of the groups of the four rides and then the extent to which. each day are members of other groups in the european forward and so i showed this to the head of the london based and year hope not hate studies of extremism in europe and is he said to me oh my goodness and others. about sixty percent of these names are well known that the other forty percent i think you just discover the new sort of right wing awful guard through through facebook so this is a kind of work that we cannot do any longer. i.
the other one is network content and else's and this is largely gauge want to know so you take a set of pages you define a set of pages so the all tried again or another won the somali just for this and other products we did have the rwandan the us for you you ask what. but which posts do they gauge with the most and then the most the left me the most could be was first shown that it's about analyzing like shares comments of posts over time and what we found and this is the opposite end of the rides their what we found largely is that. that what people engage with the most of our means.
on and it is something that's how it happens across most spaces.
and i don't all that we haven't test of the extent to which you know that this was just just a cultural blip mean i'm not talking about image macros only i'm talking about means and of a larger sense. so the idea of the mean as this sort of particular unit of cultural transmission where you you create something like you create a piece of culture and then you add it to the meme so that it's a it's a collective said of. images ideas thoughts so when you go firms from from space. movements or issue spaces or. similar in facebook what you see our or continue to the moon is is the most significant piece of called it. it's in some sense driving engagement.
ok so that's facebook where i just want to show you one other thing that we can't do any longer.
this is the use of the six russian this information pages. that. facebook and zuckerberg in particular have denied had much reach jonathan albright of the taos center for journalism columbia university of the day after the second her testimony in october two thousand and sixteen two thousand and seventeen. went to these russian descent from asian pages of facebook via crowd tango so black vests heart of texas united are muslims of america and three other russian descent from asian pages and look at their engagement scores. which were massive and has created this public tableau treme out which which shows that we're just which is still online as a data source against a gram have to hurry up.
there's only a couple more any right to integrate them into interesting into the object of study and then again arguably is as full of this trajectory of going from something quite banal. to something a little bit more news like. and what i want to talk about his of his its transformation so this is that the very first instance graham photo to this is this is kevin says drums dog and girlfriends foot.
the sort of family self is so this idea of us to calm as being as kind of play for all this is a medium for self-esteem is how it began and i told you that live monitor china's cultural analytics project took this particular idea of the study of self is very seriously i think it's still going on this.
particular work although it's become a more difficult arguably however it is in struggle has also when studying through the lens of russian disinformation or are through the lens of social causes also has become a space for studying hashtag.
the public's is the term that oftentimes used. could use other terms for as well so and then the competition between them and and in tag newsom's so antagonistic hashtag public's so black arrives matter versus or lives matter versus blue lives matter that cetera.
this is was quite evident during the just after the u.s. there's enough simply a long time ago says prix trump everything pre-trial has agreed tender sort of it for good health of the road with us to the u.s. supreme court have voted in favor of same sex marriage two thousand says. eighteen there was a number of hashtags on in sderot love wins celebrate private sector but then have the same time of their the anti-gun is to cast as a response love losers jesus rains so you can study this through sort of like you can call filter activism but you can also do.
year tag of where these were these particular post with is a tag mystic hashtag public located to this is sort of george roughy of hate if you will.
an hour days. you can do other things are so so we're scraping and. because the position other appear in june two thousand sixteen. and and then there are other sources so this is this a this is a follower ecologies is another way of studying in store on this is of one that i did recently we were commissioned to do the fake new study in the netherlands by the ministry of internal affairs and we have we found. got there's a separate vibrant right wing ecosystem lot of people study of four or fake mess.
i didn't mention adage about the style of twitter's study bots have been something similarly social media manipulation the question here is. questions of symbolic power so the use of inflated fall or constitution to to to indicate some sort of great a symbol of power. um. you tube. you tube is interesting because it's the data is so promiscuity the you tube give you everything at least they used until reasoning still giving loads and loads of data have very very interesting and point so. we developed a piece of software called love you to data to sweet nothings called on and the particular kinds of research that we do have to do with the classic rabbit hole questions of you tube so does you to push you in. two more and more extreme is content to keep he sought to keep the binge watching t.v. so addicted to this particular question more recently were trying to figure out ways of storing the politics of dilution so little more of you tube content those being taken down. our you tube this isn't this is the research the not going to talk about it today but i started a new research project on the platform or no platforming and the effects of deletion but also the removal of of accounts. but any case on.
what you can do is you can study subscription networks so who subscribes to home feature networks who features home this is the all tried again and what we found here is that it's.
the old rate is less of a movement on you tube but rather business isn't are the major altered forces are in competition when they have feet when they have business relationships they the future each other but otherwise don't.
no this is something supercool which you to this continued recently this is this is the related child networks the discontinued in may two thousand and nine and we we just finished and we just finished two studies using related child. you can do still do a related videos and come up with it. i'm just going to cause a i have five minutes. so just want to talk very very briefly about read it. and four chan.
so a lot of the work that's going on of about forty and and read a list in amsterdam is the extent to which a lot of the claims are made on fortune is that fortunes the incubator. the key baiter of particular means ideas terms. from papay the to attack and cock the rest. but then filter from for tend to read it to twitter facebook to the norm he was so this particular trajectory this kind of cross-platform and else's is in its infancy. i think that there's a lot of competition of work to be done there so how do you have successfully actually begin to think about the study of the migration of ideas from one platform to the other especially from from fortune and read it on top of them.
the. do they need you that you can do on read it. com is you can study of course abroad it's so in two thousand and fifteen. read it banned the kroon time. and. fat people hate these to celebrate its. and the question that was this was of those a computational paper on this question was was did that banning work so you can study extremism. and the propagation of ideas which could also study of the opposite of whether these bands and some things work what's interesting about that is that the users who were on croon town which is really extremist separate when they moved when they migrated internally didn't make the other severance more. more extreme. so the banning and then when and then when they went other elsewhere wasn't study the banning works for read it and so the question as you know so disbanding work only for the platforms a dozen also work for society at large as earth these of larger questions you cannot also. demarcate not the other words it's been done on writers demarcating said national space is the german read it. over the this is the dutch read it here. ok on.
yes i'm and i didn't talk much more for general that we can talk about that typically so this is some this is this is it this is the hour and a half this is the morning session i'm so what i've given you apart from the fear he of the study of where they do so. but from whether pistol mogi if you will so the how do you know something with weather. have also given you a series of sort of ideas about these the the objects of studies social performs and how to approach them and what the sort of as far as i'm concerned of these what the of needing approaches are to them now so that's it. i. three minutes for question. at it. but. and if we get. but. now it. so or. well. oh oh oh yeah and to be a year you'll know it's interesting and. who was the this recently i mean that the work that come up by with the philips and others and that's been reviewed as the transformation of the earnest web to the ironic when another it does and and and the the question of of whether our studies. these can generally speaking assume earnestness it's a to moses offer showed you are and this is also your question assumes earnestness as absurd assumes that the people aren't kind of joking around or order or not they're not trolling right side and even talk or trolling at all. no so so what you do is you make that a specific objective study right so you don't assume. but that depending on what you're doing right sort of the money on the space that you're in so what i was what i was doing with that with the antagonist it has to public's audience to grow around the supreme court's ruling i was i think rightly assuming earnestness but that should still be probed but when i get into trouble. growing spaces and you know i mean when talking about for china. in particular. you know that's that's that's not so that's a bus of the space of courtship posting so so so then you spew can change your research i would say no to studying precisely so it's not that it's not a dutch are missing. it's one doing other work it's that you have to how would be a project like that. there are questions. and it's lunchtime i think an arsenal of times greater. a coffee break.