AI4BigData is the AAAI FLAIRS special track on the application of Artificial Intelligence tools for Big Data Analysis. The track includes data-related tasks such as analysis, capture, curation, search, sharing, storage, transfer, visualization, and information credibility and privacy, with a special focus on social data on the Web. Hence, the broader context of the track comprehends AI, Web Mining, Information Retrieval, Natural Language Processing, and Sentiment Analysis.
As the Web rapidly evolves, Web users are evolving with it. In an era of social connectedness, people are becoming increasingly enthusiastic about interacting, sharing contents, and collaborating through social networks, online communities, blogs, Wikis, and other online collaborative media. In recent years, this collective intelligence has spread to many different areas, with a particular focus on fields related to everyday life such as commerce, tourism, education, and health, causing the size of the Social Web to expand exponentially. The distillation of knowledge and the assessment of its quality from such a large amount of unstructured information, which in the social media context is often diffused without any form of trusted external control, however, are extremely difficult tasks, as the contents of today’s Web are perfectly suitable for human consumption, but remain hardly accessible to machines. Furthermore, faced with this mass of often unverified content, human cognitive abilities are not sufficient to discern reliable information from fake information, and automatic techniques should be provided to tackle the online information credibility issue. By taking into consideration the above mentioned problems, the opportunity to capture the opinions of the general public about social events, political movements, company strategies, marketing campaigns, and product preferences has raised growing interest both within the scientific community, leading to many exciting open challenges, as well as in the business world, due to the remarkable benefits to be had from marketing and financial market prediction.
The main aim of this Special Track is to explore the new frontiers of big data computing for opinion mining, sentiment analysis, and credibility assessment of online information through machine learning techniques, knowledge-based systems, adaptive and transfer learning, in order to more efficiently retrieve and extract social information from the Web.
The broader context of the Special Track comprehends Information Retrieval, Natural Language Processing, Computational Social Science, Web Mining, Semantic Web, and Artificial Intelligence.
Topics of interest include but are not limited to:
- Sentiment Analysis: polarity detection and emotion recognition
- Social Network Analysis: community identification and authority discovery
- Visual Analytics: social media tools for navigation and visualization
- Social content and recommender systems
- Organization and group behavior on social media
- Cultural influences on the use and adoption of social media
- Text categorization: topic recognition and demographic identification
- Multi-modal affective computing and sentiment analysis
- Multi-domain & cross-domain evaluation
- Sentiment topic detection & trend discovery
- Predicting real-world phenomena based on social media
- Social innovation and change through social media
- Ethnographic studies of social media
- Online information credibility assessment
- Information/misinformation diffusion
- Trust and reputation in virtual communities
- Retrieval of credible information
- Gold standard datasets generation with respect to the credibility of information
- Credibility of crowdsourced data
Papers should not exceed 6 pages (4 pages for a poster) and are due by November 19, 2018. Interested authors should format their papers according to AAAI formatting guidelines, by using the latest AAAI Press Word template or LaTeX macro package.
Papers must be submitted as PDF files through the EasyChair conference system. Authors should indicate the Artificial Intelligence for Big Social Data Analysis track for submissions.
Paper submission site: https://easychair.org/conferences/?conf=flairs32
All FLAIRS papers are reviewed using a double-blind process. Author names and affiliations MUST NOT appear on submitted papers (do NOT use a fake name for your EasyChair login; your EasyChair account information is hidden from reviewers).
The proceedings of FLAIRS will be published by the AAAI. Authors of accepted papers will be required to sign a form transferring copyright of their contribution to AAAI. FLAIRS requires that there be at least one full author registration per paper.
- November 19, 2018 – Paper submission deadline
- January 21, 2019 – Paper acceptance notification
- February 4, 2019 – Poster abstract submission
- February 11, 2019 – Poster abstract notification
- February 18, 2019 – AUTHOR registration
- February 25, 2019 – Camera ready version due
- April 08, 2019 – Early registration
- May 13, 2019 – Regular registration
- May 19-22, 2019 – Conference
- Papers will be refereed and all accepted papers will appear in the conference proceedings, which will be published by AAAI Press.
Special Track Co-Chairs
Pacific Northwest National Laboratory (USA)
Email: eric.bell [at] pnnl.gov
Computer Science Department, University of Turin (Italy)
Email: patti [at] di.unito.it
Department of Informatics, Systems, and Communication (DISCo), University of Milano-Bicocca (Italy)
Email: marco.viviani [at] disco.unimib.it
- Richard Chbeir, University of Pau and Pays de l’Adour (UPPA), France
- Stefano Cresci, National Research Council (CNR), Italy
- Elisabetta Fersini, University of Milano-Bicocca, Italy
- Carlos A. Iglesias, Technical University of Madrid, Spain
- Antonio Lieto, University of Turin, Italy
- Rosa Meo, University of Turin, Italy
- Fabio Mercorio, University of Milano-Bicocca, Italy
- Manuel Montes-y-Gómez, National Institute of Astrophysics, Optics and Electronics, Mexico
- Symeon Papadopoulos, Centre for Research & Technology (CERTH), Greece
- Joe Tekli, Lebanese American University (LAU), Lebanon