Name
Abstract | ||
|---|---|---|
Many people who discuss sensitive or private issues on social media services are using pseudonyms or aliases in order to not reveal their true identity, while using their usual, non-private accounts when posting messages on less sensitive issues. Previous research has shown that if those individuals post large amounts of user-generated content, stylometric techniques can be used to identify the author based on the characteristics of the textual content. In this article we show how an author’s identity can be unmasked in a similar way using various time features (e.g., period of the day and the day of the week when a user’s posts have been published). We combine several different time features into a timeprint, which can be seen as a type of fingerprint when identifying users on social media. We use supervised machine learning (i.e., author identification) and unsupervised alias matching (similarity detection) in a number of different experiments with forum data to get an understanding of to what extent timeprints can be used for identifying users in social media, both in isolation and when combined with stylometric features. The obtained results show that timeprints indeed can be a very powerful tool for both author identification and alias matching in social media. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1186/s13388-015-0022-z | Security Informatics |
Keywords | Field | DocType |
Alias detection, Time profile, Machine learning, Social media | Data mining,Alias,World Wide Web,Social media,Computer science,Fingerprint,Artificial intelligence,Time profile,Machine learning | Journal |
Volume | Issue | ISSN |
4 | 1 | 2190-8532 |
Citations | PageRank | References |
3 | 0.42 | 22 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fredrik Johansson | 1 | 95 | 7.18 |
| Lisa Kaati | 2 | 144 | 17.65 |
| Amendra Shrestha | 3 | 33 | 4.38 |