CALL FOR PAPERS: 52nd HICSS 2019, Maui, Hawaii
January 8-11, 2019 – Maui, Hawaii
DATA ANALYTICS, DATA MINING AND MACHINE LEARNING FOR SOCIAL MEDIA MINITRACK
in the Digital and Social Media Track
URL: http://hicss.hawaii.edu/tracks-52/digital-and-social-media/
Submission Deadline: June 15, 2018 | 11:59 pm HST
Notification of Acceptance/Rejection: August 17, 2018
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CfP HICSS-52 (2019) minitrack:
Social media is changing how we work and play. It is also changing the way we access and consume media, stay in touch with family and friends, as well as how we communicate in our online communities. One of the things these activities share in common is that they generate a tremendous volume of data that can be analyzed and mined for both research and commercial purposes. This mini-track focuses on research that brings together digital and social media and data analytics, data mining & machine learning. We welcome quantitative, theoretical or applied papers whose approaches are within this scope or in closely related areas.
Topics of interest include (but are not limited to):
• Discovery, collection and extraction of social media data
• Text-, image- or video-based mining of social media content
• Opinion mining, sentiment analysis and recommendation analysis
• Cleaning, curation and provenance of data on social media
• Identifying and profiling influential participants, subgroups and communities
• Crowd or cloud computation on social media data
• Predictive and forecasting analytics based on social media content
• Trend analysis to identify emerging topics, ideas and shifts
• Visual analysis of online media structure, usage and content
• Semantic representations of online content, link analysis and linkages
• Social search, retrieval and ranking
• Performance and scalability of social media data management
• Social innovation and social entrepreneurship through social media
At recent HICSS conferences, large, complex data and information problems emerged as common themes across many of the mini-tracks. In recognition of this, we suggest that authors address topics from their own research perspective. Authors are encouraged to bring the lens of their own background and expertise to focus on data innovation issues, including analysis & mining of and also machine learning from social media data.
Minitrack Co-Chairs:
David Yates (Primary Contact)
Bentley University
dyates@bentley.edu
Dominique Haughton
Bentley University
dhaughton@bentley.edu
Kevin Mentzer
Bryant University
kmentzer@bryant.edu