Dis/Misinformation Mining from Social Media

in Special Issue   Posted on August 31, 2020 

Information for the Special Issue

Special Issue Call for Papers:

Aims and Scope

In the last 10 years, the dissemination and use of social media have grown significantly worldwide. Online social media have billions of users and are able to record hundreds of data from each of its users. The wide adoption of social media has resulted in an ocean of data which presents an interesting opportunity for performing data mining and knowledge discovery in a real-world context. The enormity and high variance of the information that propagates through large user communities influences the public discourse in society and sets trends and agendas in topics that range from marketing, education, business and medicine to politics, technology and the entertainment industry. This influence can however act as a double-edged sword, since it can also introduce threats to the community, if it is rooted in dissemination of disinformation, i.e. purposefully manipulated news and information, or misinformation, i.e. false and incorrect information, on social media. In recent years, the potential threats of dis/misinformation have been the subject of huge controversy in different domains like public healthcare systems, socioeconomics, business and politics. For instance, the circulation of scientifically invalid information and news can negatively affect the way the public responds to the outbreak of a pandemic disease, like COVID-19. Threats can also be posed to the legitimacy of an election system by enabling opponent campaigns to shape the public opinion based on conspiracy theories stemmed from false information. Mining the contents of social media to recognize the instances of misinformation and disinformation is a very first step towards immunizing the public society against the negative impacts they could introduce.

Traditional research on dis/misinformation mining from social media mainly focuses on descriptive methods such as fake news detection and propagation analysis, malicious bot detection, fact-checking social media content, and detecting the source of claims and rumors. The main distinguishing focus of this special issue will be the use of social media data for building diagnostic, predictive and prescriptive analysis models that can be used to understand how and why dis/misinformation is created and spread, to uncover hidden and unexpected aspects of dis/misinformation content, and to recommend insightful countermeasures to restrict the circulation of dis/misinformation and alleviate their negative effects. The ultimate goal is to immunize the social media against dis/misinformation and improving the trustworthiness of the social content and the socio-economic and business systems working based on the insights mined from social media. The main focus of the special issue is on proposing models and methods for tackling dis/misinformation in real-world scenarios.

In this special issue, we solicit manuscripts from researchers and practitioners, both from academia and industry, from different disciplines such as computer science, big data mining, machine learning, social network analysis and other related areas to share their ideas and research achievements in order to deliver technology and solutions for mining dis/misinformation from social media.

Topics of Interest

We solicit original, unpublished and innovative research work on all aspects around, but not limited to, the following themes:

  • Descriptive models on fake new and malicious bot detection.
  • Explainable AI for detection of dis/misinformation.
  • User behavior analysis and susceptibility prediction with regard to dis/misinformation in social media.
  • Trust and reputation in social media.
  • Dis/misinformation propagation modeling and trace analysis.
  • Prescriptive countermeasure methods against formation and circulation of misinformation
  • Predicting misinformation and bias in news on social media.
  • Predictive models for early detection of hoax spread in social media.
  • Social influence analysis on online social media including discovering influential users and social influence maximization.
  • Assessing the influence of fake news on advertising and viral marketing in social media.
  • New datasets and evaluation methodologies to help predicting dis/misinformation in social media
  • User modeling and social media including predicting daily activities, recurring events Determining user similarities, trustworthiness and reliability.
  • Social media and information/knowledge dissemination such as topic and trend prediction, prediction of information diffusion patterns, and identification of causality and correlation between events/topics/communities.
  • Merging internal (proprietary) data with social data.

Important Dates

  • Submission deadline: January 20, 2021
  • First Notification: April 1, 2021
  • Revisions Due: May 1, 2021

Submission Information

Papers submitted to this special issue for possible publication must be original and must not be under consideration for publication in any other journal or conference. Previously published or accepted conference papers must contain at least 30% new material to be considered for the special issue.

All papers are to be submitted through the journal editorial submission system. At the beginning of the submission process in the submission system, authors need to select “Mis/Dis Information Mining from Social Media” as the article type. All manuscripts must be prepared according to the journal publication guidelines which can also be found on the website provided above. Papers will be evaluated following the journal’s standard review process.

Guest Editors (Alphabetical)



Brief CV of Guest Editors

  • Ebrahim Bagheri is an Associate Professor in the Department of Electrical, Computer and Biomedical Engineering at Ryerson University. He holds a Canada Research Chair in Software and Semantic Computing as well as an NSERC Industrial Research Chair in Social Media Analytics. Ebrahim has extensive experience in developing valuable knowledge-driven intellectual property in collaboration with industrial partners and has been able to secure over $8M of funding from NSERC and Industry as PI. In 2019, he received the best systems paper award from ECIR 2019 (European Conference on Information Retrieval). He is the recipient of Young Engineer of the Year Award from Professional Engineers Ontario in 2016. He has published in venues such as CIKM, ECIR, WSDM and IP&M, just to name a few. Dr. Bagheri is a member of NSERC’s Computer Science Evaluation Group (EG1507). He is a Senior Member of IEEE and an IBM CAS Faculty Fellow.
  • 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 a text, Social Media Mining: An Introduction, Cambridge University Press. He is a founding organizer of the International Conference Series on Social Computing, Behavioral-Cultural Modeling, and Prediction, and Field Chief Editor of Frontiers in Big Data and its Specialty Chief Editor of Data Mining and Management. He is a Fellow of ACM, AAAI, AAAS, and IEEE.
  • Kai Shu is a Gladwin Development Chair Assistant Professor in the Department of Computer Science at Illinois Institute of Technology since Fall 2020. His research lies in machine learning, data mining, social computing, and their applications in disinformation, education, healthcare. He is the leading author of a monograph, Detecting Fake News on Social Media, Morgan & Claypool Publishers, and the leading editor of a book, Disinformation, Misinformation, and Fake News in Social Media, Springer Press. He obtained his Ph.D. in Computer Science at Arizona State University in July 2020. He was awarded the ASU CIDSE Doctoral Fellowship 2015 and 2020, and the 1st place of SBP Disinformation Challenge 2018. He co-presented two tutorials in ACM KDD 2019 and WSDM 2019 and has published innovative works in highly ranked journals and top conference proceedings such as ACM KDD, SIGIR, WSDM, WWW, CIKM, IEEE ICDM, IJCAI, and AAAI. He interned at Microsoft Research, Yahoo! Research, and HP Labs.
  • Fattane Zarrinkalam is a Postdoctoral Fellow at the Laboratory of Systems, Software and Semantics (LS3) at Ryerson University, where she works on projects related to Semantic-enabled Social Network Analysis. During her PhD studies, she focused on the identification of users’ interests based on their individual and collective behavior on social media specially Twitter. She has published her work in venues such as CIKM, ESWC and ECIR. In addition, she has published journal papers in premier journals including Information Retrieval and Information Processing & Management. Further, during her PhD, she was involved in four patent applications that were filed with USPTO and she has presented a tutorial on User Interest Mining from Social Media at SIGIR 2019 and KDD 2019.

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