=Paper=
{{Paper
|id=Vol-2758/OHARS-invited1
|storemode=property
|title=Mining Social Networks to Learn about Rumors, Hate Speech, Bias and Polarization - Abstract
|pdfUrl=https://ceur-ws.org/Vol-2758/OHARS-invited1.pdf
|volume=Vol-2758
|authors=Bárbara Poblete
|dblpUrl=https://dblp.org/rec/conf/recsys/Poblete20
}}
==Mining Social Networks to Learn about Rumors, Hate Speech, Bias and Polarization - Abstract==
<pdf width="1500px">https://ceur-ws.org/Vol-2758/OHARS-invited1.pdf</pdf>
<pre>
Mining Social Networks to Learn about Rumors,
Hate Speech, Bias and Polarization - Abstract
Bárbara Pobletea
a
    University of Chile, Chile


                                         Abstract
                                         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 behaviour,
                                         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 [1]. 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 [2].
                                         Especifically, 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 [3] and Galean [4]. These applications harvest collective
                                         signals created from social media text to provide a broad view of natural disasters and real-world news,
                                         respectively.

                                         Keywords
                                         Online social networks, information credibility, hate speech




Biographical Sketch
Dr. Barbara Poblete is an Associate Professor at the Computer Science (CS) Dept. of the
University of Chile. She is also a Researcher at the Millennium Institute for Foundational
Research on Data, where she co-leads the ”Fake News and Misinformation” multidisciplinary
research group. Formerly, she was a Researcher at Yahoo! Labs. Her current research areas
include Applied Machine Learning, Data Mining, Experimental Reproducibility, Online Social
Networks Analysis, Hate Speech Detection, Crisis Informatics and Information Retrieval. Her
series of work on ”information credibility in social media” (starting in 2010) have been widely
cited and were the first scientific studies addressing online misinformation in social networks.
Her research on this and other topics has appeared in Scientific American Magazine, The Wall
Street Journal, Slate Magazine, The Huffington Post, BBC News and NPR, among others.



OHARS’20: Workshop on Online Misinformation- and Harm-Aware Recommender Systems, September 25, 2020, Virtual
Event
                                       © 2020 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
    CEUR
    Workshop
    Proceedings
                  http://ceur-ws.org
                  ISSN 1613-0073
                                       CEUR Workshop Proceedings (CEUR-WS.org)



                                                                                                           1
References
[1] C. Castillo, M. Mendoza, B. Poblete, Information credibility on twitter, in: Proceedings
    of the 20th International Conference on World Wide Web, WWW ’11, Association for
    Computing Machinery, New York, NY, USA, 2011, p. 675–684. doi:1 0 . 1 1 4 5 / 1 9 6 3 4 0 5 . 1 9 6 3 5 0 0 .
[2] A. Arango, J. Pérez, B. Poblete, Hate speech detection is not as easy as you may think: A
    closer look at model validation (extended version), Information Systems (2020) 101584.
    doi:1 0 . 1 0 1 6 / j . i s . 2 0 2 0 . 1 0 1 5 8 4 .
[3] B. Poblete, J. Guzmán, J. Maldonado, F. Tobar, Robust detection of extreme events using
    twitter: Worldwide earthquake monitoring, IEEE Transactions on Multimedia 20 (2018)
    2551–2561. doi:1 0 . 1 1 0 9 / T M M . 2 0 1 8 . 2 8 5 5 1 0 7 .
[4] V. Peña-Araya, M. Quezada, B. Poblete, D. Parra, Gaining historical and international
    relations insights from social media: spatio-temporal real-world news analysis using twitter,
    EPJ Data Science 6 (2017) 25.




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