Previous pandemics, such as H1N1or SARS-CoV-1 usually occurred in certain clusters around the world, causing panic and deaths in these areas. However, recent years have seen a tremendous increase in social media channels, where users can extensively reveal their personal opinions regarding a broad array of real-life topics as they occur in real-time either by commenting, sharing, liking, or publishing posts. The proliferation of social media channels has played a major role in spreading information related to such pandemics and become critical components of emergency preparedness, response, and recovery. However, the ease of publishing any content in social media channels has facilitated the propagation of rumors, fake news, misinformation, and disinformation during and after pandemics. This exacerbated the problem by significantly causing uncertainty in the facts and emotional exploitation of a situation often exists in the form of propaganda and can be used to pursue malicious agendas. This problem became more complicated with a mixture of private opinions of individuals and state-sponsored actors, all of which can impede emergency responses and mitigations during and after pandemics, thereby causing severe consequences in different domains, including economy, politics, and health.
There is a need to develop new data technologies based on artificial intelligence, data governance, machine learning, natural language processing, and social network analysis to aid experts in analyzing large volumes of social media data in order to detect fake news, misinformation, and disinformation. A number of open challenges need more investigation from the research community, such as recent trends in composing information disorder by combining false and real content, the mechanisms that drive fake content diffusion during pandemics, how to differentiate fake content from personal viewpoints, why people tend to believe fake content and make decisions based on it during pandemics, and what are the different motivations behind the dissemination of fake content. Fact-checking and claim verification are two important strategies that worth incorporating in the automated tackling and curtailment of fake content during and after pandemics.
The main motivation for this workshop is to bring together researchers and practitioners working from different disciplines such as artificial intelligence, big data mining, machine learning, social network analysis, natural language processing, and text analytics to disseminate current research issues and advances. Only original research and technical papers describing previously unpublished, state-of-the-art research, will be considered. The aim of this workshop is to provide insight for the discussion of the major research challenges and achievements on various topics of interest.
AITDMP 2021 will be held in conjunction with the ACM International Conference on Information Technology for Social Good (GoodIT 2021) which is co-organized on September 09- 11 2021, Rome, Italy.
List of Topics
Papers on practical as well as on theoretical topics and problems in various topics related to rumors, fake news, misinformation, and disinformation during and after pandemics, are invited, with special emphasis on novel techniques and tools for automated tackling and curtailment of fake content during and after pandemics. Topics include (but are not limited to):
There is a need to develop new data technologies based on artificial intelligence, data governance, machine learning, natural language processing, and social network analysis to aid experts in analyzing large volumes of social media data in order to detect fake news, misinformation, and disinformation. A number of open challenges need more investigation from the research community, such as recent trends in composing information disorder by combining false and real content, the mechanisms that drive fake content diffusion during pandemics, how to differentiate fake content from personal viewpoints, why people tend to believe fake content and make decisions based on it during pandemics, and what are the different motivations behind the dissemination of fake content. Fact-checking and claim verification are two important strategies that worth incorporating in the automated tackling and curtailment of fake content during and after pandemics.
The main motivation for this workshop is to bring together researchers and practitioners working from different disciplines such as artificial intelligence, big data mining, machine learning, social network analysis, natural language processing, and text analytics to disseminate current research issues and advances. Only original research and technical papers describing previously unpublished, state-of-the-art research, will be considered. The aim of this workshop is to provide insight for the discussion of the major research challenges and achievements on various topics of interest.
AITDMP 2021 will be held in conjunction with the ACM International Conference on Information Technology for Social Good (GoodIT 2021) which is co-organized on September 09- 11 2021, Rome, Italy.
List of Topics
Papers on practical as well as on theoretical topics and problems in various topics related to rumors, fake news, misinformation, and disinformation during and after pandemics, are invited, with special emphasis on novel techniques and tools for automated tackling and curtailment of fake content during and after pandemics. Topics include (but are not limited to):
- AI approaches for the detection of online misinformation and disinformation
- AI approaches to identify misinformation and disinformation campaigns
- AI approaches for spotting misinformation and disinformation spreaders.
- Social media mining for automated detection of misinformation propagation and disinformation circulation
- AI approaches for automated identification and verification of claims
- AI approaches for intention detection for misinformation and disinformation contentsAI approaches for credibility assessment of Social media sources
- AI approaches for fake news curtailment, filtering and prevention.
- AI approaches for analysis/detection of distributed and multi-platform misinformation and disinformation disseminations
- AI approaches for predicting the Impact of misinformation and disinformation during pandemics
- New datasets and evaluation methodologies to aid in automated detection and analysis of misinformation and disinformation content in social media channels
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Important Dates |
Papers Submission Due: May 1, 2021
Authors Notifications: June 22, 2021 Final Manuscript Due: July 10, 2021 GoodIT 2021: September 09-11, 2021 |
Sponsored by ACM SIGCAS
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In collaboration with:
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