Disruptive technological innovations affect what lies at the heart of the fabric of our society: how people interact with each other, what they think to be true or false, and what they value as right or wrong. Indeed, concepts such as post-truth-society, filter bubbles, and echo chambers are very recent terms, and online social interactions have proven more prone to abusive and anti-social behaviors than real-world interactions. While we are starting to see the challenges posed by the pervasive adoption of these technologies, society has no answers yet.
We contend that answers to these new challenges will require a transdisciplinary approach, involving social scientists, physicists, mathematicians, computer scientists, as well as close collaboration between academic researchers, industry practitioners, and relevant government agencies. This workshop is targeted at the KDD community, and aims to chart out the area from a KDD perspective, while bringing in insights from other areas and sharing state-of the-art technologies and best practices.
- Social media content abuse detection
- Automated or incentivized activities detection
- Machine learning in adversarial environment
- Content moderation and protection of freedom of speech
- Fairness and privacy in abuse detection and mitigation
- Effect of abuses in social media on online and offline behaviors of users
- Legal and policy ramifications of anti-abuse actions
- Quality and value of UGC
- Conflict and opinions
- Opinions dynamics
- Opinion formation
- Cultural dynamics
- Polarization and controversy in social networks
- Networked journalism
- Echo chambers
- Filter bubbles
- Conformity and social influence
- Wisdom of the crowds
- Collective intelligence
- Politics in social media
- Reputation and trust
- Judgment and decision making
This is an afternoon workshop that will feature a number of (keynote) talks, and possibly a panel discussion, and a poster session.
- 1:00-1:30: Invited Talk 1: Ashish Goel (Stanford University): "Decision Making at Scale: Algorithms and Markets"
- 1:30-2:00: Invited Talk 2: Evimaria Terzi (Boston University)
- 2:00-2:30: Invited Talk 3: Kristina Lerman (University of Southern California): "Cognitive Biases and the Limits of Crowd Wisdom"
2:30 - 3:00: Coffee break
- 3:00-3:15: Contributed Talk 1: Cheng Ju, James Li, Bram Wasti and Shengbo Guo: "Semisupervised Learning on Heterogeneous Graphs and its Applications to Facebook News Feed"
- 3:15-3:30: Contributed Talk 2: Sima Sharifirad and Stan Matwin: "How is Your Mood When Writing Sexist tweets? Detecting the Emotion Type and Intensity of Emotion Using Natural Language Processing Techniques:"
- 3:30-4:00: Invited Talk 4: Shangkun Liu (Head of Youku Video Search and Content Security, Alibaba Inc): "Challenges and Practical Learning in Vision Based Censorship of Social Content"
- 4:00-4:30: Invited Talk 5: Brad Chen and Christian Schuler (YouTube): "Adversarial System Design at Scale"
- 4:30-5:00: Invited Talk 6: Damon McCoy (New York University): "On the Effectiveness of Interventions in the Abuse Ecosystem"
- Ashish Goel, Stanford University
Ashish Goel is a Professor of Management Science and Engineering and (by courtesy) Computer Science at Stanford University, and a member of Stanford's Institute for Computational and Mathematical Engineering. He received his PhD in Computer Science from Stanford in 1999, and was an Assistant Professor of Computer Science at the University of Southern California from 1999 to 2002. His research interests lie in the design, analysis, and applications of algorithms; current application areas of interest include social networks, participatory democracy, Internet commerce, and large scale data processing. Professor Goel is a recipient of an Alfred P. Sloan faculty fellowship (2004-06), a Terman faculty fellowship from Stanford, an NSF Career Award (2002-07), and a Rajeev Motwani mentorship award (2010). He was a co-author on the paper that won the best paper award at WWW 2009, and an Edelman Laureate in 2014.
Professor Goel was a research fellow and technical advisor at Twitter, Inc. from July 2009 to Aug 2014
Title: Decision Making at Scale: Algorithms and Markets
YouTube competes with Hollywood as an entertainment channel, and also supplements Hollywood by acting as a distribution mechanism. Twitter has a similar relationship to news media, and Coursera to Universities. But there are no online alternatives for making democratic decisions at large scale as a society. In this talk, we will describe some algorithmic and market-inspired approaches towards large scale decision making that we are exploring. In particular, we will describe three recent results:
(1) We will describe our platform for participatory budgeting, which has been used in over 40 elections. We will then show how a series of incremental votes can lead to an optimum solution to many budgeting problems. The incremental voting algorithms are inspired by prediction markets, where each subsequent participant provides a small correction to the market
(2) We will describe how one can construct a market for public-decision-making inspired by the celebrated work of Foley and others on public good markets
(3) We will describe a deliberation mechanism where a group comes to a decision by a series of pairwise negotiations. We will show that this results in provably good decisions on median spaces.
The above results are in increasing order of interaction among decision makers -- in the first, individuals are reacting to an entire decision made by the rest of the society; in the second, individuals are participants in a market that looks very much like a traditional Fisher market, and in the third, participants interact with other participants directly as opposed to via aggregated prices.
This represents joint work with Brandon Fain, Nikhil Garg, Vijay Kamble, David Marn, Kamesh Munagala, Benjamin Plaut, and Sukolsak Sakshuwong.
- Evimaria Terzi, Boston University
Evimaria Terzi is an Associate Professor at the Computer Science Department at Boston University. Before joining BU in 2009, she was a research scientist at IBM Almaden Research Center. Evimaria has received her Ph.D. from University of Helsinki, Finland and her MSc from Purdue University. Evimaria is a recipient of the Microsoft Faculty Fellowship (2010) and the NSF CAREER award (2012). Her research interests span a wide range of data-mining topics including algorithmic problems arising in online social networks, social media and recommender systems.
- Kristina Lerman, University of Southern California
Kristina Lerman is a Principal Scientist at the University of Southern California Information Sciences Institute and holds a joint appointment as a Research Associate Professor in the USC Computer Science Department. Trained as a physicist, she now applies network- and machine learning-based methods to problems in social computing and social media analysis. Her work explores how cognitive heuristics and network structure bias our perceptions of online information.
Title: Cognitive Biases and the Limits of Crowd Wisdom
The many decisions people make about what information to attend to affect emerging trends, the diffusion of information in social media, and performance of crowds in peer evaluation tasks. Due to constraints of available time and cognitive resources, the ease of discovery strongly affects how people allocate their attention. Through empirical analysis and online experiments, we identify some of the cognitive heuristics that influence individual decisions to allocate attention to online content and quantify their impact on individual and collective behavior. Specifically, we show that the position of information in the user interface strongly affects whether it is seen, while explicit social signals about its popularity increase the likelihood of response. These heuristics become even more important in explaining and predicting behavior as cognitive load increases. The findings suggest that cognitive heuristics and information overload bias collective outcomes and undermine the “wisdom of crowds” effect.
Call for papers
Papers of up to 8 pages length are solicited (but shorter papers are encouraged). Submissions must be in PDF, written in English, and formatted in ACM Sigconf Proceedings Style.
Accepted papers will be included in an informal workshop proceedings volume that will be distributed with the registration materials at the main conference. They will be presented as contributed talks in the workshop.
Please submit your paper via the EasyChair Link (closed now).
- Workshop paper submissions:
May 15, 2018.
- Workshop paper notifications:
June 8, 2018.
- Workshop Day: August 20, 2018
（All deadlines are at 11:59 PM Pacific Standard Time)
- Aris Anagnostopoulos, Sapienza University of Rome
- Shih-Fu Chang, Senior Executive Vice Dean, Columbia Engineering, Columbia University
- Cristian Canton, Engineering Manager for Computer Vision at Community Integrity, Facebook
- Kiran Garimella, Aalto University
- Manuel Gomez Rodriguez, MPI for Software Systems
- David Kempe, University of Southern California
- Gavin Li, Director of Youku Data Intelligence, Alibaba
- Kamesh Munagala, Duke University
- Michael Mathioudakis, University of Helsinki
- Kathleen McKeown, Henry and Gertrude Rothschild Professor of Computer Science, Columbia University
- Xi Chen, Ghent University
- Tijl De Bie, Ghent University
- Aristides Gionis, Aalto University
- Vlad Gorelik, Engineering Manager for Community Integrity, Facebook
- Antonis Matakos, Aalto University
- Michael McNally, Engineering Director for News Feed Integrity, Facebook
- Ruben van der Dussen, Director of Thorn's Innovation Lab
- Min Shao, Director of Engineering, YouTube
- Panayiotis Tsaparas, University of Ioannina
Contact us at: email@example.com