Name
Abstract | ||
|---|---|---|
People use social media resources like Twitter, Facebook, forums etc. to shareand discuss various activities or topics. By aggregating topic trends acrossmany individuals using these services, we seek to construct a richer profileof a person's activities and interests as well as provide a broader context ofthose activities. This profile may then be used in a variety of ways tounderstand groups as a collection of interests and affinities and anindividual's participation in those groups. Our approach considers that muchof these data will be unstructured, free-form text. By analyzing free-form text directly, we may be able to gain an implicit grouping ofindividuals with shared interests based on shared conversation, and not onexplicit social software linking them. In this paper, we discuss aproof-of-concept application called Grandmaster built to pull short sections oftext, a person's comments or Twitter posts, together by analysis andvisualization to allow a gestalt understanding of the full collection of allindividuals: how groups are similar and how they differ, based on theirtext inputs. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/ICDMW.2015.187 | ICDM Workshops |
Field | DocType | Citations |
Data mining,Conversation,Computer science,Social software,Artificial intelligence,Analytics,World Wide Web,Data visualization,Social media,Visualization,Gestalt psychology,Tag cloud,Machine learning | Conference | 0 |
PageRank | References | Authors |
0.34 | 8 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nathan Fabian | 1 | 107 | 8.00 |
| Warren L. Davis IV | 2 | 1 | 2.04 |
| Elaine M. Raybourn | 3 | 150 | 28.72 |
| Kiran Lakkaraju | 4 | 445 | 36.90 |
| Jonathan Whetzel | 5 | 5 | 1.85 |