Profile characteristics of fake Twitter accounts
| Autor: | Jeanna Matthews, Brian R Voter, Supraja Gurajala, Joshua S. White, Brian Hudson |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Information Systems and Management
business.industry Communication Internet privacy Measure (physics) lcsh:A 020206 networking & telecommunications 02 engineering and technology Library and Information Sciences Popularity Computer Science Applications Politics 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Business lcsh:General Works Information Systems |
| Zdroj: | Big Data & Society, Vol 3 (2016) |
| ISSN: | 2053-9517 |
| DOI: | 10.1177/2053951716674236 |
| Popis: | In online social networks, the audience size commanded by an organization or an individual is a critical measure of that entity’s popularity and this measure has important economic and/or political implications. Such efforts to measure popularity of users or exploit knowledge about their audience are complicated by the presence of fake profiles on these networks. In this study, analysis of 62 million publicly available Twitter user profiles was conducted and a strategy to identify automatically generated fake profiles was established. Using a combination of a pattern-matching algorithm on screen-names and an analysis of update times, a reasonable number (∼0.1% of total users) of highly reliable fake user accounts were identified. Analysis of profile creation times and URLs of these fake accounts revealed their distinct behavior relative to a ground truth data set. The characteristics of friends and followers of users in the two data sets further revealed the very different nature of the two groups. The ratio of number of followers-to-friends for ground truth users was ∼1, consistent with past observations, while the fake profiles had a median ratio ∼30, indicating that the fake users we identified were primarily focused on gathering friends. An analysis of the temporal evolution of accounts over 2 years showed that the friends-to-followers ratio increased over time for fake profiles while they decreased for ground truth users. Our results, thus, suggest that a profile-based approach can be used for identifying a core set of fake online social network users in a time-efficient manner. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|