{"id":455,"date":"2019-01-27T18:00:11","date_gmt":"2019-01-27T18:00:11","guid":{"rendered":"http:\/\/deberker.com\/archy\/?p=455"},"modified":"2019-04-28T20:16:08","modified_gmt":"2019-04-28T20:16:08","slug":"abuse-detection-on-twitter-a-collaboration-with-amnesty-international","status":"publish","type":"post","link":"https:\/\/deberker.com\/archy\/abuse-detection-on-twitter-a-collaboration-with-amnesty-international\/","title":{"rendered":"Abuse detection on Twitter: a collaboration with Amnesty International"},"content":{"rendered":"

At the NeurIPS 2018 workshop on AI for Social Good<\/a> we presented a piece of work we performed in collaboration with Amnesty International.  We leveraged a mixture of crowdsourcing and deep learning to study the nature and quantity of abuse suffered by prominent women on twitter.<\/p>\n

At this point quite a lot has been written about the project: if you’re interested in the technical side you can check out our paper <\/a>and the more extended methodological note<\/a>; Amnesty have a very nice interactive description <\/a>of the findings ; and there was an extraordinary amount of news coverage, from Forbes<\/a> to Wired<\/a>.<\/p>\n

 <\/p>\n

\"\"
My chief contribution to the work: an abuse classifier utilising pretained BERT language models.<\/figcaption><\/figure>\n

From the NLP team, our major contribution was an abuse classifier than outperformed Google’s API when tested upon this dataset. It was based upon the hugely influential BERT model<\/a>, and relied upon a superb open-source implementation of BERT from HuggingFace<\/a>. This is ironic – there’s an overarching ‘anti-big-tech’ narrative to which we were arguably contributing, and yet our tooling relied upon pre-trained models from Google and a framework (Pytorch) from Facebook. This serves to underline the fact that these big companies, Twitter included, produce an enormous variety of impacts – some hugely positive, others overwhelmingly negative. Few companies are unambiguously bad (or good).<\/p>\n

One of the most interesting – and unexpected- outcomes of the work was the (albeit temporary) hit that Twitter’s stock suffered. Following the report, an apparently quite influential analyst from Citron Research downrated the stock<\/a>,  causing a 12% plummet – something like $2.5 billion of value lost.<\/p>\n

\"\"
The report was released December 17th, followed by a precipitous decline in Twitter’s value. Note, however, the reasonably rapid re-adjustment.<\/figcaption><\/figure>\n

 <\/p>\n

 <\/p>\n

 <\/p>\n

 <\/p>\n

 <\/p>\n

 <\/p>\n

 <\/p>\n

The violence of the market’s response to our work suggests that we hit a nerve. I think that we were probably confirming something that people already knew (Twitter is a toxic place for some women), but hadn’t had the quantitative basis to assert so forcefully.<\/p>\n

The bounce-back in the following month might hint that Twitter’s response to the report was effective, reassuring investors’ of imminent action – or simply that markets are flighty and volatile places.<\/p>\n

 <\/p>\n","protected":false},"excerpt":{"rendered":"

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