Name
Abstract | ||
|---|---|---|
Twitter is a short-text message system developed 6 years ago. It now has more than 100 million users generating over 300 million tweets every day. Twitter accounts are used for diverse purposes, such as social, advertising, political, religious, benevolent or vicious ideologies, among other activities. These activities can be communicated by humans, a machine or a robot. The purpose of this paper is to build predictive models, such as Logistic Regression, K Nearest Neighbors and Neural Network in order to identify the best variables that help predict, based on the contents, whether the tweets are coming from a human or a machine with the least possible error. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-58628-1_40 | Lecture Notes in Artificial Intelligence |
Keywords | Field | DocType |
Twitter,Social media,Predictive models | k-nearest neighbors algorithm,World Wide Web,Social media,Computer science,Ideology,Artificial neural network,Robot,Logistic regression,Politics | Conference |
Volume | ISSN | Citations |
10284 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 3 | 12 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| John Khoury | 1 | 4 | 1.02 |
| Charles Li | 2 | 0 | 1.01 |
| Chloe Lo | 3 | 0 | 0.68 |
| Corinne Lee | 4 | 0 | 0.68 |
| Shakeel Rajwani | 5 | 0 | 0.68 |
| David Woolfolk | 6 | 0 | 0.34 |
| Alexis-Walid Ahmed | 7 | 0 | 0.68 |
| Loredana Crusov | 8 | 0 | 0.34 |
| Arnold Pérez-Goicochea | 9 | 0 | 0.34 |
| Christopher Romero | 10 | 0 | 0.34 |
| Rob French | 11 | 0 | 0.34 |
| Vasco Ribeiro | 12 | 0 | 0.34 |