Name
Abstract | ||
|---|---|---|
Twitter accounts are used for a multitude of reasons, including social, commercial, political, religious, and ideological purposes. The wide variety of activities on Twitter may be automated or non-automated. Any serious attempt to explore the nature of the vast amount of information being broadcast over such a medium may depend on identifying a potentially useful set of content features hidden within the data. This paper proposes a set of content features that may be promising in efforts to categorize social media activities, with the goal of creating predictive models that will classify or estimate the probabilities of automated behavior given certain account content history. Suggestions for future work are offered. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-58628-1_42 | Lecture Notes in Artificial Intelligence |
Keywords | Field | DocType |
Twitter,Social media,Content feature extraction | Data science,Broadcasting,Categorization,Social media,Multitude,Computer science,Ideology,Feature extraction,Politics | Conference |
Volume | ISSN | Citations |
10284 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 5 | 13 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shai Neumann | 1 | 4 | 2.03 |
| Charles Li | 2 | 0 | 1.01 |
| Chloe Lo | 3 | 0 | 0.68 |
| Corinne Lee | 4 | 0 | 0.68 |
| Shakeel Rajwani | 5 | 0 | 0.68 |
| Suraj Sood | 6 | 0 | 0.34 |
| Buttons A. Foster | 7 | 0 | 0.34 |
| Toni Hadgis | 8 | 0 | 0.34 |
| Yaniv Savir | 9 | 0 | 0.34 |
| Frankie Michaels | 10 | 0 | 0.34 |
| Alexis-Walid Ahmed | 11 | 0 | 0.68 |
| Nikki Bernobic | 12 | 0 | 0.34 |
| Markus Hollander | 13 | 0 | 0.34 |