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https://www.journals.elsevier.com/information-processing-and-management
Special Issue on Dis/Misinformation Mining from Social Media
http://ls3.rnet.ryerson.ca/?page_id=1077
AIM 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 improve 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 FOR THE SPECIAL ISSUE
* Submission deadline: January 20, 2021
* First Notification: April 1, 2021
* Revisions Due: May 1, 2021
GUEST EDITORS (Alphabetical)
* Ebrahim Bagheri, Ryerson University, Toronto, Canada, bagheri@ryerson.ca
* Huan Liu, Arizona State University, Arizona, United States, huanliu@asu.edu
* Kai Shu, Illinois Institute of Technology, Chicago, Illinois, kshu@iit.edu
* Fattane Zarrinkalam, Ryerson University, Toronto, Canada, fzarrinkalam@ryerson.ca
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.
Calls for Submission
Call for Proposals - Information Processing & Management Journal
Tuesday, October 27, 2020
to Wednesday, January 20, 2021
Event Details
CALL FOR PAPERS
Information Processing & Management Journal - Special Issue on Dis/Misinformation Mining from Social Media
Impact Factor: 4.787
https://www.journals.elsevier.com/information-processing-and-management
Special Issue on Dis/Misinformation Mining from Social Media
http://ls3.rnet.ryerson.ca/?page_id=1077
AIM 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 improve 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 FOR THE SPECIAL ISSUE
* Submission deadline: January 20, 2021
* First Notification: April 1, 2021
* Revisions Due: May 1, 2021
GUEST EDITORS (Alphabetical)
* Ebrahim Bagheri, Ryerson University, Toronto, Canada, bagheri@ryerson.ca
* Huan Liu, Arizona State University, Arizona, United States, huanliu@asu.edu
* Kai Shu, Illinois Institute of Technology, Chicago, Illinois, kshu@iit.edu
* Fattane Zarrinkalam, Ryerson University, Toronto, Canada, fzarrinkalam@ryerson.ca
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.