Deception detection in Twitter
| Autor: | Ugo Buy, Sohaib Ghani, Mohamed F. Mokbel, Jalal S. Alowibdi, Philip S. Yu |
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| Rok vydání: | 2015 |
| Předmět: |
Information retrieval
Computer science Communication media_common.quotation_subject Deception computer.software_genre Computer Science Applications Human-Computer Interaction Set (abstract data type) Naive Bayes classifier Media Technology Data mining Cluster analysis Lying computer Analysis method Information Systems media_common |
| Zdroj: | Social Network Analysis and Mining. 5 |
| ISSN: | 1869-5469 1869-5450 |
| DOI: | 10.1007/s13278-015-0273-1 |
| Popis: | Online Social Networks (OSNs) play a significant role in the daily life of hundreds of millions of people. However, many user profiles in OSNs contain deceptive information. Existing studies have shown that lying in OSNs is quite widespread, often for protecting a user’s privacy. In this paper, we propose a novel approach for detecting deceptive profiles in OSNs. We specifically define a set of analysis methods for detecting deceptive information about user genders and locations in Twitter. First, we collected a large dataset of Twitter profiles and tweets. Next, we defined methods for gender guessing from Twitter profile colors and names. Subsequently, we apply Bayesian classification and K-means clustering algorithms to Twitter profile characteristics (e.g., profile layout colors, first names, user names, and spatiotemporal information) and geolocations to analyze the user behavior. We establish the overall accuracy of each indicator through extensive experimentation with our crawled dataset. Based on the outcomes of our approach, we are able to detect deceptive profiles about gender and location with a reasonable accuracy. |
| Databáze: | OpenAIRE |
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