Data-driven Approaches on Understanding Disinformation

Data-driven Approaches on Understanding Disinformation

Overview

Arguably, one of the greatest inventions of humanity is the Web. Despite the fact it revolutionized our lives, the Web has also introduced or amplified a set of several social issues like the spread of disinformation and hateful content to a large number of people.

In this seminar, we will look into research that focuses on extracting insights from large corpus of data with the goal to understand emerging socio-technical issues on the Web such as the dissemination of disinformation and hateful content. We will read, present, and discuss papers that follow a data-driven approach to analyze large-scale datasets across several axes to study the multi-faceted aspects of emerging issues like disinformation.

During this seminar, the participants will have the opportunity to learn about state-of-the-art techniques and tools that are used for large-scale processing, including, but not limited to, statistical techniques, machine learning, image analysis, and natural language processing techniques.

Logistics

Location: online via Zoom

Lecturers: Savvas Zannettou and Yang Zhang

Assistants: Min Chen (min.chen@cispa.saarland), Xinlei He (xinlei.he@cispa.saarland), Yang Zou (yang.zou@cispa.saarland)

Time: Every Wednesday 14:00-15:30

List of Papers

1. Vosoughi, S., Roy, D. and Aral, S., 2018. The spread of true and false news online. Science, 359(6380), pp.1146-1151.
2. Zannettou, S., Caulfield, T., De Cristofaro, E., Kourtellis, N., Leontiadis, I., Sirivianos, M., Stringhini, G. and Blackburn, J., 2017, November. The web centipede: understanding how web communities influence each other through the lens of mainstream and alternative news sources. In Proceedings of the 2017 Internet Measurement Conference (pp. 405-417).
3. Starbird, K., 2017, May. Examining the alternative media ecosystem through the production of alternative narratives of mass shooting events on Twitter. In Eleventh International AAAI Conference on Web and Social Media.
4. Resende, G., Melo, P., Sousa, H., Messias, J., Vasconcelos, M., Almeida, J. and Benevenuto, F., 2019, May. (Mis) Information Dissemination in WhatsApp: Gathering, Analyzing and Countermeasures. In The World Wide Web Conference (pp. 818-828).
5. Zannettou, S., Caulfield, T., Setzer, W., Sirivianos, M., Stringhini, G. and Blackburn, J., 2019, June. Who let the trolls out? towards understanding state-sponsored trolls. In Proceedings of the 10th acm conference on web science (pp. 353-362).
6. Jiang, S., Robertson, R.E. and Wilson, C., 2019, July. Bias misperceived: The role of partisanship and misinformation in youtube comment moderation. In Proceedings of the International AAAI Conference on Web and Social Media (Vol. 13, No. 01, pp. 278-289).
7. Kumar, S., West, R. and Leskovec, J., 2016, April. Disinformation on the web: Impact, characteristics, and detection of wikipedia hoaxes. In Proceedings of the 25th international conference on World Wide Web (pp. 591-602).
8. Budak, C., Agrawal, D. and El Abbadi, A., 2011, March. Limiting the spread of misinformation in social networks. In Proceedings of the 20th international conference on World wide web (pp. 665-674). 
9. Zannettou, S., Caulfield, T., Blackburn, J., De Cristofaro, E., Sirivianos, M., Stringhini, G. and Suarez-Tangil, G., 2018, October. On the origins of memes by means of fringe web communities. In Proceedings of the Internet Measurement Conference 2018 (pp. 188-202).
10. Mathew, B., Dutt, R., Goyal, P. and Mukherjee, A., 2019, June. Spread of hate speech in online social media. In Proceedings of the 10th ACM Conference on Web Science (pp. 173-182).
11. Mariconti, E., Suarez-Tangil, G., Blackburn, J., De Cristofaro, E., Kourtellis, N., Leontiadis, I., Serrano, J.L. and Stringhini, G., 2019. " You Know What to Do" Proactive Detection of YouTube Videos Targeted by Coordinated Hate Attacks. Proceedings of the ACM on Human-Computer Interaction, 3(CSCW), pp.1-21.
12. Finkelstein, J., Zannettou, S., Bradlyn, B. and Blackburn, J., 2018. A quantitative approach to understanding online antisemitism. arXiv preprint arXiv:1809.01644.
13. Chandrasekharan, E., Pavalanathan, U., Srinivasan, A., Glynn, A., Eisenstein, J. and Gilbert, E., 2017. You can't stay here: The efficacy of reddit's 2015 ban examined through hate speech. Proceedings of the ACM on Human-Computer Interaction, 1(CSCW), pp.1-22.
14. Mathew, B., Saha, P., Tharad, H., Rajgaria, S., Singhania, P., Maity, S.K., Goyal, P. and Mukherjee, A., 2019, July. Thou shalt not hate: Countering online hate speech. In Proceedings of the International AAAI Conference on Web and Social Media (Vol. 13, No. 01, pp. 369-380).
15. Zannettou, S., Blackburn, J., De Cristofaro, E., Sirivianos, M. and Stringhini, G., 2018, June. Understanding web archiving services and their (mis) use on social media. In Twelfth International AAAI Conference on Web and Social Media.
16. Ribeiro, M.H., Ottoni, R., West, R., Almeida, V.A. and Meira Jr, W., 2020, January. Auditing radicalization pathways on youtube. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (pp. 131-141).

Schedule

Grading

The course will be graded by 50% presentation, 30% reports, and 20% class performance.