Name
Abstract | ||
|---|---|---|
The successful use of social media to manipulate public opinion via bots and hired individuals to spread (mis)information to unsuspecting users reached alarming levels due to the manipulations during the 2016 US elections and the Brexit deliberations in the UK. Fake interaction such as "liking" and "retweeting" are staged to foster trust in the posts of bots and individuals, which makes it difficult for individuals to detect the posts that are part of greater schemes. We propose an approach based on supervised learning to classify collections of tweets as "organized" when they inhabit premeditated intent and as "organic" otherwise. Features related to users and posting behavior are used to train the classifiers using 851 data sets totaling above 270 million tweets. Further classifiers are trained to assess the effectiveness of the selected features. The random forest algorithm persistently yielded the best results with scores greater than 95% for both accuracy and f-measure. For comparison purposes, unsupervised learning methods were used to cluster the same data sets. The Gaussian Mixture Model clustered [organized vs organic] data set with 99% agreement with the labels. The success of using only behavioral features to detect organized behavior is encouraging. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1142/S0218213019600017 | INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS |
Keywords | Field | DocType |
Organized behavior detection, supervised learning, social media analysis, big data, Twitter, political propaganda | World Wide Web,Social media,Computer science,Supervised learning,Artificial intelligence,Public opinion,Big data,Machine learning | Journal |
Volume | Issue | ISSN |
28 | 6 | 0218-2130 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Erdem Beğenilmiş | 1 | 0 | 0.34 |
| Suzan Uskudarli | 2 | 10 | 5.19 |