Assessing Trustworthiness in Social Media
A Social Computing Approach
Social media is gaining popularity in recent years and increasingly becoming an integral part of our life. Given the
extensiveness, instantaneity, and diffusion speed of social media, e.g., a tweet or a clip of video, could galvanize a
digital revolution or wreak havoc with one's otherwise routine and uneventful working life. With the presence of
adversaries, the convenient use of and low barrier of social media brings about new challenges. How well we address
these challenges can directly influence our ability to manage information and misinformation, and the future role of social
media as a reliable communication mechanism. One such pressing challenge is to assess information trustworthiness in social
media . We propose to investigate research issues related to social media trustworthiness and its assessment by leveraging
social research methods, developing new computational social methods, and creating novel approaches to social media data
collection and sharing.
In social sciences, trust is about a relationship between two entities, the trustor and the trustee. Trust can be
defined as the perception of the trustor about the degree to which the trustee would satisfy an expectation.
Trustworthiness can be defined from the perspective of both entities; in this work, it
is the perspective of the trustor that defines a property that can be judged, i.e., the amount of trust associated
with the trustee. In all cases trust is a heuristic decision rule, allowing the human to deal with complexities
that would require unrealistic effort in rational reasoning. One of the key current challenge is to rethink how
the rapid progress of technology has impacted trust as information technology has significantly changed how
people interact, express themselves, and behave. The assessment of information trustworthiness in social
media requires answers to the three essential questions about the information: (1) source (or author), (2)
author position, and (3) content. The search for the answers is greatly complicated by the nature of social
messages, and inordinate amounts of data and meta data. In addition, both linked data and attribute data are
present in social media. The former represents the connectedness among entities and the latter the properties
of entities. In search of the three answers, we face research challenges:
In this project, we focus on developing computational social theories and methods for the first two challenges.
The third challenge is partially addressed in our recent work. Additional work on trust maintenance can be found in
- Information Provenance - Identifying the true source (or author) of information,
- Friendship Differentiation - Determining if the author is a friend, acquaintance, or foe, and
- Content Analysis - Analyzing the content to ascertain its intention, quality, and etc.
Social Networks, Social Media, Social Media Mining, Trust, Information Provenance.
- PhD Dissertations
- Pritam Gundecha. "Managing a User's Vulnerability on a Social Networking Site", Arizona State University, March, 2015. (link)
- Jiliang Tang. "Computing Distrust in Social Media", Arizona State University, February, 2015. (link)
- Reza Zafarani, Mohammad Ali Abbasi, and Huan Liu. "Social Media Mining: An Introduction", Cambridge University Press, ISBN: 9781107018853. May, 2014. (link)
- Geoffrey Barbier, Zhuo Feng, Pritam Gundecha, and Huan Liu. "Provenance Data in Social Media", Synthesis Lectures on Data Mining and Knowledge Discovery, Morgan
& Claypool Publishers, 2013. (link)
- Journal Articles
- Jiliang Tang, Huiji Gao, Atish Das Sarma, Yingzhou Bi, and Huan Liu. "Trust Evolution: Modeling and Its Applications", IEEE Transactions on Knowledge and Data Engineering (TKDE), Forthcoming.
- Jiliang Tang, Yi Chang and Huan Liu. "Mining Social Media with Social Theories: A Survey", SIGKDD Explorations.
- Pritam Gundecha, Geoffrey Barbier, Jiliang Tang and Huan Liu. "User Vulnerability and its Reduction on a Social Networking Site", Journals of Transactions on Knowledge Discovery from Data, (TKDD 2014), Forthcoming.
- Jiliang Tang, Xia Hu and Huan Liu.``Social Recommendation: A Review'', Social Network Analysis and Mining, 2013
- Conferences and Workshops
- Jiliang Tang, Chikashi Nobata, Anlei Dong, Yi Chang and Huan Liu. "Propagation-based Sentiment Analysis for Microblogging Data", SIAM International Conference on Data Mining (SDM), 2015.
- Jiliang Tang, Shiyu Chang, Charu Aggarwal, and Huan Liu. "Negative Link Prediction in Social Media", ACM International Conference on Web Search and Data Mining (WSDM), 2015.
- Ying Wang, Xin Wang, Jiliang Tang, Wanli Zuo, and Guoyong Cai. "Modeling Status Theory in Trust Prediction", the AAAI Conference on Artificial Intelligence (AAAI), 2015.
- Jiliang Tang, Xia Hu, Yi Chang, and Huan Liu. "Predictability of Distrust with Interaction Data", ACM International Conference on Information and Knowledge Management (CIKM), 2014.
- Jiliang Tang, Xia Hu and Huan Liu. "Is Distrust the Negation of Trust? The Value of Distrust in Social Media", ACM Hypertext conference, 2014.
- Mohammad Ali Abbasi, Jiliang Tang, and Huan Liu. "Scalable Learning of Users' Preferences Using Networked Data". ACM Hypertext conference, 2014.
- Xia Hu, Jiliang Tang, and Huan Liu. "Leveraging Knowledge across Media for Spammer Detection in Microblogging", the 37th Annual ACM Special Interest Group on Information Retrieval Conference (SIGIR 2014).
- Xia Hu, Jiliang Tang, and Huan Liu. "Online Social Spammer Detection". the 28th AAAI Conference on Artificial Intelligence (AAAI 2014).
- Suhas Ranganath, Pritam Gundecha, and Huan Liu. "A Tool for Assisting Provenance Search in Social Media", demonstration paper, the 22nd ACM International Conference on Information and Knowledge Management (CIKM 2013).
- Pritam Gundecha, Zhuo Feng, and Huan Liu. "Seeking Provenance of Information in Social Media", short paper, the 22nd ACM International Conference on Information and Knowledge Management (CIKM 2013).
- Jiliang Tang, Huiji Gao, Xia Hu, and Huan Liu. "Context-Aware Review Helpfulness Rating Prediction", the 7th ACM Recommender Systems Conference (RecSys 2013).
- Zhuo Feng, Pritam Gundecha, and Huan Liu. "Recovering Information Recipients in Social Media via Provenance", short paper, the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013).
- Pritam Gundecha, Suhas Ranganath, Zhuo Feng, and Huan Liu. "A Tool for Collecting Provenance Data in Social Media", demonstration paper, the 19th ACM SIGKDD International Conference On Knowledge Discovery and Data Mining (SIGKDD 2013).
- Jiliang Tang, Xia Hu, Huiji Gao, and Huan Liu. "Exploiting Local and Global Social Context for Recommendation", the 23rd International Joint Conference on Artificial Intelligence (IJCAI 2013).
- Jiliang Tang, Huiji Gao, Xia Hu, and Huan Liu. "Exploiting Homophily Effect for Trust Prediction"(hTrust), the 6th ACM International Conference on Web Search and Data Mining (WSDM 2013).
- Jiliang Tang, Huiji Gao, Huan Liu and Atish Das Sarma. "eTrust: Understanding Trust Evolution in an Online World", the Eighteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2012), 2012.
- Jiliang Tang, Huiji Gao, and Huan Liu. "mTrust: Discerning Multi-Faceted Trust in a Connected World", the 5th ACM International Conference on Web Search and Data Mining (WSDM 2012), February 8-12, 2012. Seattle, Washington
- Technical Reports
- Pritam Gundecha, and Huan Liu. "Minimizing User Vulnerability and Retaining Social Utility in Social Media", ASUCISE-2011-006, School of Computing,
Informatics, and Decision Systems Engineering, Arizona State University, AZ 85287. Nov. 2011.
- Invited to join live panel discussion on "Selling Personal Data" on HuffPost Live, Huffington Post, July 30, 2013. [link]
- Scholarship was awarded to Pritam Gundecha to attend the "Summer School on Formal Methods for the Science of Security", July 22-26, 2013 at the UIUC. [link]
Project Members (current and former)
This material is based upon work supported by, or in part by, the U.S. Army Research Office (ARO) under contract/grant number 025071.
Any opinions, findings, and conclusions or recommendations expressed here are those of the author(s) and do not necessarily reflect the views of ARO.
Created by Huan Liu who can be reached
Webmasters: Pritam Gundecha, Email: pritamATasu.edu;
Jiliang Tang, Email: jiliang.tangATasu.edu
Last Updated: April 14, 2015