ABSTRACT
Post-specific diffusion network elucidates the who-saw-from-whom paths of a post on social media. A diffusion network for a specific post can reveal trustworthy and/or incentivized connections among users. Unfortunately, such a network is not observable from available information from social media platforms; hence an inference mechanism is needed.
In this paper, we propose an algorithm to infer the diffusion network of a post, exploiting temporal, textual, and network modalities. The proposed algorithm identifies the maximum likely diffusion network using a conditional point process. The algorithm can scale up to thousands of shares from a single post and can be implemented as a real-time analytical tool. We analyze inferred diffusion networks and show discernible differences in information diffusion within various user groups (i.e. verified vs. unverified, conservative vs. liberal) and across local communities (political, entrepreneurial, etc.). We discover differences in inferred networks showing disproportionate presence of automated bots, a potential way to measure the true impact of a post.
- Project: DiffuScope. https://sites.google.com/view/diffuscope/home.Google Scholar
- F. A. Chowdhury, L. Allen, M. Yousuf, and A. Mueen. On twitter purge: A retrospective analysis of suspended users. In Companion Proceedings of the Web Conference 2020, pages 371--378, 2020.Google ScholarDigital Library
- F. A. Chowdhury, D. Saha, M. R. Hasan, K. Saha, and A. Mueen. Examining factors associated with twitter account suspension following the 2020 us presidential election. arXiv preprint arXiv:2101.09575, 2021.Google Scholar
- C. A. Davis, O. Varol, E. Ferrara, A. Flammini, and F. Menczer. BotOrNot: A System to Evaluate Social Bots. In Proceedings of the 25th International Conference Companion on World Wide Web - WWW '16 Companion, pages 273--274, New York, New York, USA, 2016. ACM Press.Google ScholarDigital Library
- N. Du, L. Song, M. G. Rodriguez, and H. Zha. Scalable influence estimation in continuous-time diffusion networks. In Advances in neural information processing systems, pages 3147--3155, 2013.Google ScholarDigital Library
- M. Gomez-Rodriguez, J. Leskovec, and A. Krause. Inferring networks of diffusion and influence. ACM Transactions on Knowledge Discovery from Data (TKDD), 5(4):1--37, 2012.Google Scholar
- A. G. Hawkes. Point spectra of some mutually exciting point processes. Journal of the Royal Statistical Society: Series B (Methodological), 33(3):438--443, 1971.Google ScholarCross Ref
- A. G. Hawkes. Spectra of some self-exciting and mutually exciting point processes. Biometrika, 58(1):83--90, 1971.Google ScholarCross Ref
- D. Kempe, J. Kleinberg, and E. Tardos. Maximizing the spread of influence through a social network. In Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '03, page 137--146, New York, NY, USA, 2003. Association for Computing Machinery.Google ScholarDigital Library
- J. Leskovec, L. Backstrom, and J. Kleinberg. Meme-tracking and the dynamics of the news cycle. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 497--506, 2009.Google ScholarDigital Library
- S. A. Myers and J. Leskovec. Clash of the contagions: Cooperation and competition in information diffusion. In 2012 IEEE 12th international conference on data mining, pages 539--548. IEEE, 2012.Google ScholarDigital Library
- S. A. Myers and J. Leskovec. The bursty dynamics of the twitter information network. In Proceedings of the 23rd international conference on World wide web, pages 913--924, 2014.Google ScholarDigital Library
- M. E. Newman. Modularity and community structure in networks. Proceedings of the national academy of sciences, 103(23):8577--8582, 2006.Google ScholarCross Ref
- D. M. Romero, B. Meeder, and J. Kleinberg. Differences in the mechanics of information diffusion across topics: idioms, political hashtags, and complex contagion on twitter. In Proceedings of the 20th international conference on World wide web, pages 695--704, 2011.Google ScholarDigital Library
- I. Taxidou and P. M. Fischer. Online analysis of information diffusion in twitter. In Proceedings of the 23rd International Conference on World Wide Web, pages 1313--1318, 2014.Google ScholarDigital Library
- S. Vosoughi, D. Roy, and S. Aral. The spread of true and false news online. Science, 359(6380):1146--1151, 2018.Google ScholarCross Ref
- J. Yang and J. Leskovec. Modeling information diffusion in implicit networks. In 2010 IEEE International Conference on Data Mining, pages 599--608. IEEE, 2010.Google ScholarDigital Library
- J. Zhang, B. Liu, J. Tang, T. Chen, and J. Li. Social influence locality for modeling retweeting behaviors. In IJCAI, volume 13, pages 2761--2767, 2013.Google ScholarDigital Library
- J. Zhang, P. S. Yu, and Y. Lv. Organizational chart inference. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 1435--1444, 2015.Google ScholarDigital Library
- Q. Zhao, M. A. Erdogdu, H. Y. He, A. Rajaraman, and J. Leskovec. Seismic: A self-exciting point process model for predicting tweet popularity. In Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining, pages 1513--1522, 2015.Google ScholarDigital Library
Index Terms
- DiffuScope: inferring post-specific diffusion network
Recommendations
Identifying the influential bloggers in a community
WSDM '08: Proceedings of the 2008 International Conference on Web Search and Data MiningBlogging becomes a popular way for a Web user to publish information on the Web. Bloggers write blog posts, share their likes and dislikes, voice their opinions, provide suggestions, report news, and form groups in Blogosphere. Bloggers form their ...
Disinformation Warfare: Understanding State-Sponsored Trolls on Twitter and Their Influence on the Web
WWW '19: Companion Proceedings of The 2019 World Wide Web ConferenceOver the past couple of years, anecdotal evidence has emerged linking coordinated campaigns by state-sponsored actors with efforts to manipulate public opinion on the Web, often around major political events, through dedicated accounts, or “trolls.” ...
Rumor Gauge: Predicting the Veracity of Rumors on Twitter
Special Issue on KDD 2016 and Regular PapersThe spread of malicious or accidental misinformation in social media, especially in time-sensitive situations, such as real-world emergencies, can have harmful effects on individuals and society. In this work, we developed models for automated ...
Comments