Mining Misinformation in Social Media:
Understanding Its Rampant Spread, Harm, and Intervention

Overview

A rapid increase in social networking services in recent years has enabled people to share and seek information effectively. Meanwhile, the openness and timeliness of social networking sites also allow for the rapid creating and dissemination of misinformation. As witnessed in recent incidents of fake news, misinformation escalates quickly and can impact social media users with undesirable consequences and wreak havoc instantaneously. Despite many people have been aware of that fake news and rumors are misleading the public and even compromising elections, the problem is not going away. In this tutorial, we will discuss how misinformation gains traction in the race for attention, introduce emerging challenges of identifying misinformation, present a comparative survey of current data mining research in tackling the challenges, and suggest available resources and point to directions for future work.

This tutorial will target researchers and practitioners who are interested in the area of misinformation mining and have basic knowledge of network analysis, data mining, and machine learning. It will be delivered at a college junior/senior level, and should be easily accessible to interested parties from both industry and academia.

References

• [1] Liang Wu, Fred Morstatter, Xia Hu, and Huan Liu. Mining Misinformation in Social Media. Big Data in Complex and Social Networks, pages 123-152, 2016. pdf
• 
  Early Detection of Misinformation
• [2] Justin Sampson, Fred Morstatter, Liang Wu, and Huan Liu, Leveraging the Implicit Structure within Social Media for Emergent Rumor Detection, CIKM 2016. pdf
• [3] Liang Wu, Jundong Li, Xia Hu, Huan Liu, Gleaning Wisdom from the Past: Early Detection of Emerging Rumors in Social Media, SDM 2017. pdf
• [4] Liang Wu, Huan Liu, Tracing Fake-News Footprints: Characterizing Social Media Messages by How They Propagate. WSDM 2018. pdf
• 
  Spread of Misinformation in Social Media
• [5] Xiaoyan Qiu, Diego FM Oliveira, Alireza Sahami Shirazi, Alessandro Flammini, and Filippo Menczer. Limited individual attention and online virality of low-quality information. Nature Human Behavior 1 (2017): 0132. pdf
• [6] Giovanni Luca Ciampaglia, Alessandro Flammini, and Filippo Menczer. The production of information in the attention economy. Scientific reports 5 (2015). pdf
• [7] Chengcheng Shao, Giovanni Luca Ciampaglia, Onur Varol, Alessandro Flammini, and Filippo Menczer. The spread of fake news by social bots. arXiv preprint 2017pdf
• 
  Camouflaged Misinformation Spreader Detection
• [8] Liang Wu, Xia Hu, Fred Morstatter, Huan Liu, Adaptive Spammer Detection with Sparse Group Modeling. ICWSM 2017 pdf
• [9] Liang Wu, Xia Hu, Fred Morstatter, Huan Liu. Detecting Camouflaged Content Polluters. ICWSM 2017 pdf

Syllabus

Section	Duration
Introduction Definition of misinformation	10 minutes
Misinformation detection Challenges of misinformation detection Early detection of misinformation	30 minutes
Spread of misinformation in social media Reasons behind spread of misinformation Modeling the spread of misinformation	30 minutes
Misinformation spreader detection Challenges of misinformation spreader detection Detection of misinformation spreaders	30 minutes
Available resources and collection of data	20 minutes
Q&A	10 minutes

Presenters

• Liang Wu has been a PhD student of Computer Science and Engineering at Arizona State University since August, 2014. He obtained his master's degree from Chinese Academy of Sciences in 2014 and bachelor's from Beijing Univ. of Posts and Telecom., China in 2011. The focus of his research is in the areas of misinformation and content polluter detection, and statistical relational learning. He has published over 20 innovative works in major international conferences in data mining, such as ICDM, SDM, WSDM and ICWSM. Liang has participated in various competitions and data challenges and won the Honorable Mention Award of KDD Cup 2012 on predicting click-through rate of search sponsored ads, ranking 3rd on leaderboard. He is also an author of 5 patent applications and 2 book chapters, and he is a tutorial speaker at SBP'16. He has been a Research Intern at Microsoft Research Asia and a Data Science Intern at Etsy. More information can be found at http://www.public.asu.edu/~liangwu1.
• Giovanni Luca Ciampaglia is an assistant research scientist at the Indiana University Network Science Institute (IUNI). He holds a Ph.D. in Informatics from the University of Lugano, Switzerland and a M.Sc. (Laurea) from Sapienza University of Rome, Italy. Before joining IUNI he was an analyst for the Wikimedia Foundation, and a research associate at the Swiss Federal Institute of Technology in Zurich. He won the 2012 Kantar "Information is Beautiful" for Best Interactive Visualization and has been the recipient of a postdoctoral "Mobility" fellowship from the Swiss National Science Foundation. His research is supported by the Democracy Fund. His interests are in the emerging disciplines of network science and computational social science, with a particular focus on information diffusion on the Internet and social media, and the spread of misinformation. He serves on the editorial board of the Journal of Computational Social Science and on the program committee of several conferences. His research has been covered in major news outlets, including the Wall Street Journal, the Economist, Wired, MIT Technology Review, NPR, and CBS News, to cite a few. More can be found at http://glciampaglia.com/.
• Huan Liu is a professor of Computer Science and Engineering at Arizona State University. He obtained his Ph.D. in Computer Science at University of Southern California and B.Eng. in Computer Science and Electrical Engineering at Shanghai JiaoTong University. Before he joined ASU, he worked at Telecom Australia Research Labs and was on the faculty at National University of Singapore. At Arizona State University, he was recognized for excellence in teaching and research in Computer Science and Engineering and received the 2014 President's Award for Innovation. His research interests are in data mining, machine learning, social computing, and artificial intelligence, investigating interdisciplinary problems that arise in many real-world, data-intensive applications with high-dimensional data of disparate forms such as social media. His well-cited publications include books, book chapters, encyclopedia entries as well as conference and journal papers. He is a co-author of Social Media Mining: An Introduction by Cambridge University Press. He serves on journal editorial boards and numerous conference program committees, and is a founding organizer of the International Conference Series on Social Computing, Behavioral-Cultural Modeling, and Prediction. He is an IEEE Fellow. More can be found at http://www.public.asu.edu/~huanliu.