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<h3>THE 24TH PACIFIC-ASIA CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING</h3>

Workshop on Data Science for Fake News


Scope:

The contributed program in this workshop will be primarily formed by a number of research papers presenting new research on data science for fake news. These would cover data science research for fake news, that could be traditionally be under the following heads in terms of methods: * Graph Analytics * Natural Language Processing * Deep Learning * Computer Vision * Image Processing * Affective Computing * Game Theory * Information Retrieval * Social Media Analytics * Spatio-temporal Data Mining * Unsupervised Learning * Semi-supervised Learning * Supervised Learning * Crowdsourcing and Active Learning

We will particularly encourage papers that cover multiple such methodological building blocks in building a technology to combat fake news.

Program Schedule:

  • 10:00 - 11:00am (10 mins for Q&A)

    • Talk: Some Lessons Learned in Detecting and Explaining Fake News on Social Media
    • Keynote Speaker: Huan Liu and Kai Shu, Arizona State University, USA

  • 11:00 - 11:15am

    • Break

  • 11:15 - 11:35am (5 mins for Q&A)

    • Talk: Adversarial Deep Factorization for Recommender Systems
    • Speakers: Ziqing Chen (University of Waterloo), Yiwei Zhang (Kings college London), and Zongyang Li (The University of Sydney)

  • 11:35 - 11:55am (5 mins for Q&A)

    • Talk: Detection of spammers using modified diffusion convolution neural network
    • Speakers: Hui Li (School of Software, Dalian University of Technology), Wenxin Liang (School of Software, Dalian University of Technology) and Zihan Liao (School of Software, Dalian University of Technology)

  • 11:55 - 12:15am (5 mins for Q&A)

    • Talk: Dynamics Of Online Toxicity In The Asia-Pacific Region
    • Speakers: Thomas Marcoux (University of Arkansas at Little Rock), Adewale Obadimu (University of Arkansas at Little Rock) and Nitin Agarwal (University of Arkansas at Little Rock)

Keynote

Title: Some Lessons Learned in Detecting and Explaining Fake News on Social Media

Huan Liu and Kai Shu
Arizona State University, Tempe, AZ

Abstract

In recent years, fake news has become a global phenomenon, particularly so during the pandemic, COVID-19. The wide dissemination of fake news can have detrimental societal effects on individuals and the society. Despite the recent progress in detecting fake news, it remains a challenging task due to its complexity, diversity, multi-modality, speed, and costs of fact-checking or annotation. In this talk, we look at some lessons learned when exploring effective and explainable detecting strategies of fake news, and discuss challenges in fake news research and the need for interdisciplinary research.

Bio

Huan Liu is a professor of Computer Science and Engineering at Arizona State University. Before he joined ASU, he worked at Telecom Australia Research Labs and was on the faculty at National University of Singapore. He is a co-author of a text, Social Media Mining: An Introduction, Cambridge University Press and a recent monograph, Detecting Fake News on Social Media, Morgan & Claypool Publishers. 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. More can be found at http://www.public.asu.edu/~huanliu.

Kai Shu is a Ph.D. candidate and research assistant in the Data Mining and Machine Learning (DMML) Lab at Arizona State University. His research lies in machine learning, data mining, social computing, and 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 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 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. More can be found at http://www.public.asu.edu/~skai2/.

Organizing Committee:

The key organizers would be the co-chairs of this workshop, who will be: * Tanmoy Chakraborty, Indraprastha Institute of Information Technology, [email protected] * Deepak P, Queen’s University Belfast, [email protected] * Cheng Long, Nanyang Technological University, [email protected] * Santhosh Kumar G., Cochin University of Science and Technology, [email protected]

Web Chair: Hridoy Sankar Dutta, Indraprastha Institute of Information Technology, [email protected]

Important Dates:

Paper submission due (Final): January 24, 2020 January 31, 2020
Acceptance notification: February 29, 2020

Submission and Peer Review

Regular papers (maximum 8 pages) should be prepared in English and follow the Springer Lecture Notes in Computer Science (LNCS) template, downloadable from here. All papers must be submitted via our EasyChair submission page and will go through a single-blind peer-review process. Only manuscripts in PDF or Microsoft Word format will be accepted.