Co-located with the 10th International Conference on Computational Data and Social Networks (CSoNet 2021), Nov 15-17, Montreal (Canada).
Aim & Scope
We live in an era where people are witnessing floods of data online, generated as a result of performing online activities, mainly social media. Some people publish deceptive or fabricated online stories, posts, and news to attract online users, change their state of mind, or make political or financial gains. It is typically carried out with the help of automatic bots to accelerate fake news dissemination. A multitude of research is going on for tracing the surge of falsehoods through automated fact-checking techniques. The developed solution should be fully automated, accountable, instant, and accurate. It should be able to extract sentences from textual or audio clips, distinguish between facts, opinions, and questions, examine and match the data, and finally yield the results with proper explanations. Therefore, Artificial Intelligence is the best suite solution that is being applied for the verification of the claims made in the published stories or news. Specifically, this process involves using Natural Language Processing (NLP), deep learning algorithms, and other AI tools to find, monitor, and match claims. There is vast potential in AI to control fake news spread on the Internet with the help of automated fact-checking. It involves societal, political, ethical, and financial aspects of society. It is an emerging domain that still requires many improvements in approach, tools, and platforms. This workshop aims at providing a platform for academic and industrialist researchers and practitioners to exchange and publish the challenges, latest research trends, and results on fact-checking, fake news, and malware detection in online social networks (OSNs).
The topics relevant to this special issue include but are not limited to:
- Techniques for detecting fake news
- Social media and fake news
- Blockchain for fake news detection
- Characterization of fake news
- Automated fact-checking
- Solutions for fact-checking
- Deep learning techniques for fact-checking
- Challenges in fact-checking using AI
- AI for detecting fake news on Social Media
- Techniques for identifying the source of spread of fake news
- Optimization methods for fact-checking ad fake news detection
- Ethical aspects and social media
- Artificial intelligence and its ethical and legal issues
- AI for automated information retrieval
- Different machine learning techniques for fake news detection and fact-checking
- Limitations of automated information retrieval
- Ethical issues in information retrieval
- Effects of social media on the proliferation of fake news
- Techniques and tools for mitigating viral marketing
- Schemes for secure multimedia data sharing on OSNs
- Privacy-preserving co-ownership model for co-owned multimedia data management
- Collaborative privacy management
- Steganalytic software and mechanisms to find hidden information within multimedia data
- Methodologies, techniques, and tools for digital oblivion
- Metadata removal and security
- Malware detection for social networks
- Defense mechanisms against malware propagation in OSNs
- Sybil defense and fake profile detection
- Phishing and spammer detection
- Built-in OSNs security solutions
- Profile-cloning detection
- Secure integration of Cyber-Physical Systems (CPS) with social network
- Secure socio-CPS networks
June 20, 2021 July 24, 2021 August 2, 2021 (firm)
Journal Publication: A selected set of top-quality papers may be invited to submit full papers to a special issue.
The topics of the workshop and how they relate to CSoNet
The interest in online social networks (OSNs) is evident from the growing number of conferences and workshops dedicated to the topic as well as journal special issues including industrial applications (i.e., increased interest in the use of OSNs across many applications and significant investment by different industries in their development). This workshop will serve as a forum for sharing experiences and findings relating to protocol design, real testbeds, experimental evaluation, prototyping, empirical characterization, and empirical results on fact-checking, fake news, and malware detection in online social networks (OSNs). The workshop welcomes regular papers and poster proposals for novel work / work-in-progress on fact-checking, fake news, and malware detection in online social networks (OSNs). It is also encouraged to submit research works related to the development of testbeds, measurement platforms, and innovative prototypes.
- Dr. Arcangelo Castiglione, University of Salerno, Fisciano, Italy. E-mail: email@example.com
- Dr. B. B. Gupta, National Institute of Technology, Kurukshetra, India. E-mail: firstname.lastname@example.org
- Prof. Xiaojun Chang, Monash University Clayton Campus, Australia.
- Prof. Marjan Kuchaki Rafsanjani, Shahid Bahonar University of Kerman, Kerman, Iran.
- Dr. Gianni D’Angelo, University of Salerno, Fisciano, Italy.
- Dr. Raffaele Pizzolante, University of Salerno, Fisciano, Italy.
Proposed Technical Program Committee (tentative)
- Prof. Shiyan Hu, University of Southampton, UK.
- Prof. Laurence T. Yang, St Francis Xavier University, Antigonish, Canada.
- Dr. Lidia Ogiela, Pedagogical University in Krakow, Poland.
- Dr. Alessio Merlo, University of Genova, Genova, Italy.
- Prof. Xinyi Huang, Fujian Normal University, Fuzhou, China.
- Prof. Marek Ogiela, AGH University of Science and Technology, Krakow, Poland.
- Prof. Florin Pop, University Politehnica of Bucharest, Bucharest, Romania.
- Prof. Debiao He, Wuhan University, Wuhan, China.
- Prof. Francesco Colace, University of Salerno, Fisciano, Italy.
- Prof. Weizhi Meng, Technical University of Denmark, Lyngby, Denmark.
- Dr. Christian Esposito, University of Salerno, Fisciano, Italy.
- Dr. Xiaokang Wang, St Francis Xavier University, Antigonish, Canada.
Planned review procedures
We will abide by the manuscript submission and review method and Due Dates set by CSoNet 2021. Three independent reviews of each paper by researchers of the respective field have no conflict of interest with the authors.