Mining Social Networks to Learn about Rumors, Hate Speech, Bias and Polarization


Online social networks are a rich resource of unedited user-generated multimedia content. Buried within their day-to-day chatter, we can find breaking news, opinions and valuable insight into human behavior, including the articulation of emerging social movements. Nevertheless, in recent years social platforms have become fertile ground for diverse information disorders and hate speech expressions. This situation poses an important challenge to the extraction of useful and trustworthy information from social media. In this talk I provide an overview of existing work in the area of social media information credibility, starting with our research in 2011 on rumor propagation during the massive earthquake in Chile in 2010. I discuss, as well, the complex problem of automatic hate speech detection in online social networks. In particular, how our review of the existing literature in the area shows important experimental errors and dataset biases that produce an overestimation of current state-of-the-art techniques. Specifically, these issues become evident at the moment of attempting to apply these models to more diverse scenarios or to transfer this knowledge to languages other than English. As a particular way of dealing with the need to extract reliable information from online social media, I talk about two applications, Twically and Galean. These applications harvest collective signals created from social media text to provide a broad view of natural disasters and real world news, respectively.


Dr Bárbara Poblete is an Associate Professor at the Computer Science Department of the Universidad de Chile and an Amazon Visiting Scholar at Alexa Shopping Research She is also a Researcher at the Millennium Institute on Data IMFD Chile), where she co leads the “Fake News and Misinformation” multidisciplinary research group Formerly, she was a researcher at Yahoo! Labs Her research areas are Social Network Analysis, Web Data Mining, Crisis Informatics and Applied Machine Learning Her influential work “Information Credibility on Twitter” was awarded the 2021 IW 3 C 2 Seoul Test of Time Award at The Web Conference This was the first paper to address misinformation in social media and has been widely cited, featured in SciAM WSJ, Slate, The Huffington Post, BBC News and NPR, among others She holds 7 US Patents and has over 80 peer reviewed articles Currently (according to Google Scholar), she is the most cited female Computer Scientist in Chile and among the top cited in Latin America She is also co founder of ChileWiC the and now main event in Chile that gathers women in CS and Technology, which has now been running for 9 years.}



15:00 – 16:00 (Chilean time).

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