Opt Out of Online Sexism – Open Source Activism
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Transcript: English(auto-generated)
00:03
So, good afternoon, my name is Theresa Ingram and I am founder of the non-profit OptOut that aims to build tools to help women and female identifying people re-engage with healthy online debate. Just a warning before I begin this talk, there will be some extreme language, so
00:20
just FYI. So before I tell you about OptOut, I would like to tell you first about why we need to exist. So Diane Abbott, number two of the Labour Party in Britain, during the 2017 general election,
00:40
Amnesty International did a study on all of the tweets that all MPs received. Of those classified as hateful, Diane Abbott received 45.1% of them. Laurie Penny, a journalist, writer, activist, often writing about feminist issues, by the
01:01
first real bomb threat. And Johanna Schmidt-Nielsen, former leader of the Danish left-wing political party in Liston, alongside the usual barrage of threats against her body and harassment, it was once reported by a troll that she was in fact dead.
01:22
Now you might be sitting there thinking, okay, great Theresa, but men and male identifying people also suffer abuse online. And you know, these women that you're describing, they are public personas, so maybe they should be expecting something like this. But really, the stats speak for themselves.
01:41
It's not just public facing women that suffer this. Women are twice as likely to be sexually harassed online as men, mainly affecting our young women. 90% of all victims of revenge porn are female, and women are twice as likely to suffer adverse consequences as a result of this online abuse.
02:03
Women and female identifying people disproportionately suffer online abuse, particularly those who challenge the status quo. Online sexism is real, and it's silencing the voices that society so desperately needs to hear.
02:22
So at Opt Out, we're aiming to put a stop to the silencing nature of online sexism. We're building tools to help all female identifying people who've got something to say get back to the online spaces that they've been chased out from. We're doing this by not only building tools, but by also building a movement.
02:44
By holding workshops that give female identifying people a chance to come together and share their experiences, we're not only building the important vital social infrastructure that these people need, but we're also allowing them to come together and in doing so, act
03:01
in a form of protest. By helping to form this community, we are able to spot needed technical infrastructure and make sure that the tools that we build are fit for purpose, ensuring that our tech is as community driven as possible.
03:20
In addition to our workshops, we aim to build a website that supports women getting their voices back. Inspired by HarassMap, an Egyptian based NGO that you can see here, where an individual can submit reports of individual physical harassment, which then gets displayed to an online map.
03:42
We will build our website to allow somebody to anonymously submit their experiences. This data will be stored, studied and feed the models that our tools depend on. Our website will also transparently show details of what we're doing with the data and also the impact that our tools are having for women across the world, hoping to fuel
04:05
the movement. Our long term goal is to have a virtual HarassMap, which shows which communities on your chosen social media platform are sexist, sexually aggressive or just downright nasty,
04:22
enabling women and female identifying people to navigate the murky waters of online society as best they can. So, the opt out ethos, the General Data Protection Regulation or GDPR has changed our lives on social media platforms. We have the right to be forgotten, to dictate what is being recorded about us and to opt
04:45
out if we wish, but the abuse of women and female identifying people suffer online is not avoidable. We see opt out as an extension of the GDPR that also protects the human rights of these people online, allowing them to join in online debate once more.
05:07
So, what tools are we talking about? Alongside the website and the workshops, our main idea is a browser extension that filters out online sexism from an individual social media feed. And it does so by a sentiment, classification sentiment analyzer.
05:23
As you can see here, apologies, the video is not brilliant, you can't see the button, but you get the picture. So, currently our tool works on Twitter and we've got a very, very simple neural net behind it that's trained on 10,000 of your normal troll tweets, but nothing sexism specific.
05:43
Our plan going forward is to retune this model with a sexism-labeled data set from Zira Qazeem and his co-workers, and once this is done and our website is up and running and the word has been spread, hopefully we will start generating a larger
06:02
sexism data set. But we're going to need to annotate this data set, and we are proposing to do so with the two-round annotation scheme. Taking inspiration from Zirak once again and his co-workers, we're going to first label
06:20
based on the categories generalized, directed, explicit and implicit. So, here are some examples of what that actually looks like in terms of language. Generalized, all students are lazy. Directed, you are a lazy student, which we may already have heard in our lives. Explicit, the candidate did not write enough papers, and implicit, the candidate was not
06:43
an innovative researcher. But language is nuanced and complicated, and it can be combinations of all of these and also sexist. So, for example, the first comment there is both generalized, using the bridezilla
07:01
word, and then also directed, because it's directed at somebody, and similar sort of thing for the second. It's important though, even though this is going to be a challenge, that we identify what is explicitly sexist first, because if we are to encourage respectful debate and
07:23
avoid creating any unintended echo chambers or biases with our tool, we need to get rid of the really obvious stuff first, and then understand the implicit, implied sexism later. So, once we've done this initial round of annotations, we're then going to further
07:43
classify the comments based on five different labels taken from Maria and Savino's misogyny labels. What we have here underneath the different labels are tweet examples. So, discredit, slurring over women with no larger other intention.
08:06
Stereotype and objectification, to make women subordinate or description of a woman's physical appearance and slash all comparisons to narrow standards. And then dominance, to preserve male control, protect male interests, and to exclude women
08:23
from conversation. Sexual harassment and threats of violence, to physically assert power over women or to intimidate and silence women through threats. And derailing, to justify abuse, reject male responsibility, and attempt to disrupt
08:41
the conversation in order to refocus it. With this two-level annotation scheme, we hope we will be able to identify the different faces of online sexism. So, in addition to this data annotation and understanding, we're going to deploy what
09:01
I call the three C's approach. So, content, which is what I've already previously discussed, context, and conversation. So, content will be using the sentiment analyzer with the labelings I just talked about. Context, so who is the abuser in relation to the target?
09:21
Are they part of a bigger mob attack? This is important to know. And then conversation. Has the sentiment of the conversation between the two taken a sustained nosedive? This could be an indication of intimate partner violence and requires a very different solution to what we're offering.
09:40
With these labels and a better understanding of the behaviors and relationships of online sexism, we'll be better informed to answer the age-old question of you know it when you see it, which is characteristic of online sexism. And so once this is all done, we can start to build and test different models and really
10:01
start to make a difference to women and female identifying people all over the globe. But what's the coolest thing, which I really, really like about our tool, is that we are consent-focused, meaning that we aim to block what an individual finds distressing and not what we think.
10:22
We're doing this by deploying a technique that I call big sister instead of big brother, where there will be a local instance of the model in somebody's browser that they can supply feedback to with the simple click of a button. The data stays locally, but people will be encouraged to share their labelings with
10:43
opt-out via the website. By focusing on individual consent and not a one-model fits all approach, we ensure that the diverse range of online interactions are not stifled, but that productive and respectful interactions can flourish.
11:08
Enabling female identifying people to join Healthy Debate is only possible if we also ensure that these people are safe online. We plan to do this by utilizing the moderators that most social media platforms have
11:22
effectively. Whenever our sentiment analyzers detect abuse, the comments will go automatically to the moderators with a traffic light labeling scheme, allowing them to prioritize more effectively what tweets or what comments need attention immediately.
11:42
This ensures that the user safety is never compromised. Once this is all said and done, in the years down the line when we have a great little NGO behind us, we are going to develop the browser extension. We are going to have a functionality that allows people to just use a blacklist
12:05
of accounts, so these people are automatically blocked from an individual's social media. These will be maintained and shared by what we call digilantes, which are groups of people that are seeded from the workshops that we are going to be holding that
12:22
also act like a support network for anybody who has suffered online sexism. We then have the automatic replacement of comments, like I just described, and then finally a sentiment dashboard that pops up before the page loads with a traffic light labeling scheme for each comment, allowing the user to preemptively
12:44
decide what they do and they don't want to see. We have a lot to do, as you can see, but we are planning to get a working product by the end of August, and then it will be so popular, we will get a huge
13:01
dataset straightaway, and then we can start planning with that in September, by the end of September, and then what is really important is that we move across to different languages. We are going to design the web app so that all you will need to do is change the dataset and maybe some hyper parameters, and you can change the language
13:23
from English to Spanish to Romanian to whatever you would like. This will enable us to build the community that we want behind it, because online sexism is not restricted just to English. So with a topic like this, I think it is really important for me to tell you
13:44
all who is behind it. We are a bunch of volunteers at the moment, apart from myself. I'm working out of savings, but most people are just working in their free time. We are a group of people from social scientists to data nerds,
14:01
but there is one characteristic that we all share. We won't let hate win. Our vision, we want to champion women back into the online world they have been chased out from, support them and their voices while still protecting them, and holding perpetrators accountable.
14:21
We need to exist. If you share our vision, if you believe in the cause, I ask you to join us, even if it is just by talking to somebody about the issue, about what I discussed today, mentoring, co-contributions, going to the GitHub, star us, all this stuff.
14:42
I'm a relative novice. I've got about a year and a half worth of software engineering experience, but the community has rallied behind me an incredible amount, and this ship is sailing. So if you'd like to get on board, just let me know. Online sexism has to stop. Let's opt out.
15:16
If there's any questions, you can use the mics in the...
15:30
Not sexist to say ladies first. Yeah, thank you for not being a sexist. But this stand is discriminating my height. Yeah.
15:41
I think it's a really good idea. I'm really impressed by what you and your team are doing. I noticed one thing that I think is a really, really good idea, which is really customized to a certain user, what he or she found that is offensive, and then it's not one model fits all.
16:01
But that also raised a question in my mind. That may be like technical challenges to kind of make it a customized model. It may require a lot of resources. So have your team figured out what the approach is to overcome this challenge? I would really like to know. If not, then maybe we could find a solution to do it.
16:22
No, so please, let's talk after this. Let's talk about that. Yeah, thank you. Well, first of all, congratulations on your wonderful talk, wonderful explanation. Congratulations for the project itself.
16:43
Thank you. When sometimes it's too much easy to pretend that things doesn't exist or just happens to the others. However, I would like to ask you more about the technical infrastructure that you developed, if you don't mind to clarify it a bit.
17:02
Oh, you would like me to discuss? Yes, of course. So the browser extension is currently using Keras and TensorFlow. That's obviously for the NLP stuff. But it's a very, very simple model. It's not even using any RNN or LTSM.
17:24
It's very, very simple. And the back end is just in a nice simple flask gap. We all make mistakes. It should have been Django, but there we go. It's fine. But yeah, it's a very lightweight thing at the moment.
17:41
What we're really focusing on is just trying to understand the science behind it first. So we're putting a lot of effort into research and getting different data sets and playing around with them. The actual web infrastructure is a bit thin on the ground. But if there are any front-end developers that would like to join, please, because I have no front-end experience.
18:02
So that would be really great. And we don't have a front-ender at the moment. Any more questions? Feedback? Cool. You mentioned switching data sets to switch languages. Why do you need that? Why can't you put everything into one data set?
18:21
Are there things that exist in one language and not in the other? Or is it too much data? I just presumed we'd need to do that. I just presumed we'd need to do that, because I think sexism is so different in different languages. But that's an advantage. Because it's different, you can put it all in the same pot
18:41
and it won't disturb each other. That is a very, very good point. I don't know. I'm not a data scientist. Thanks. Thank you for your talk. Really nice.
19:00
I actually have a question about business models, a non-technical question, because I'm curious. I guess all of those social medias now allow to flag offensive content, right? You say you want to develop a browser extension, but do you know how effective this offensive flagging is? And it takes some time, I guess.
19:22
Sorry, are you saying that there's already something similar that the social media platforms are offering? It's another approach, right? Facebook, I guess you're targeting Twitter. It allows to flag offensive content. But the individual still has to see it. And so by filtering it out, you just don't see it at all.
19:40
You never see it. Exactly. And also, Twitter, Facebook, I have heard incidences when somebody has reported something and they've turned around and said, no, you're wrong. Or it's been very, very long for them to do something and to take down the comments.
20:01
So what this is trying to do is just, because if you are, for example, a politician and you socialize online, but you say something and then your feed is full of misogynistic or sexist abuse, it dilutes what you're really trying to do,
20:22
which is just read the news or talk with friends. And so with this way of filtering it, the good stuff remains. All right, thank you. Thank you for your talk. Are there plans to collaborate with social media platforms
20:42
that, for example, if a person has a lot of tweets or comments that get flagged by a wide range of women, that, for example, accounts get blocked or something like that? That would be great. And, you know, Twitter, I mean, this is a problem that a lot of the social media platforms are being pressured to solve.
21:03
So it would be great if at some point we could be incorporated with the platforms. We've not received an incredible amount of support, let's put it like that. So at some point it would be great too, but yeah, we'll see. Okay, thank you.
21:22
I have a question. What kind of formats do you plan to support on the comments? Like in some social media platforms, like Twitter, I suppose, a lot of the problems that people will experience also come in images or maybe audio or video. Do you have some plans to tackle those?
21:40
Or maybe in the back line at some point? At some point, yeah. Yeah, at some point. But for now just the, yeah, just tweet, just text. Right.
22:05
Going once, going twice. I'll ask them. Do you have many people using the extensions right now, or is it live or is it just like daily?
22:21
It's not live yet. Testing, experimenting, proof of concept. Yeah, exactly. We've got a proof of concept and we're hoping to take it to, there's a funding body called the Prototype Fund in Berlin in August. Mozilla is also based in Berlin, so we're only going to bring this out on Firefox to begin with
22:41
because Chrome do have something similar, but if you, for example, if you hit their API, the perspective API, with you are a feminist, it comes back as toxic. So their one-size-fits-all approach is just not fitting anybody. So we're going to stick with Mozilla, maybe get some funding there. Living the dream.
23:01
But yeah. So no, it's not live yet, but it will be soon. I've just struggled with Amazon Web Services for a week, so. So you just mentioned you hope you might get funding from Mozilla.
23:22
And you also said you're right now mostly operating it out of pocket, basically. So do you have any other funding plans? Yeah, so the Prototype Fund, which fits us perfectly, so their categories are things like, you know, internet health,
23:41
and we already have some contacts within the Prototype Fund, so that's really the one that we're focusing on. Cool, I hope that works out. Thank you.