Bot Datasets on Twitter: Analysis and Challenges
The reach and influence of social networks over modern society and its functioning have created new challenges and opportunities to prevent the misuse or tampering of such powerful tools of social interaction. Twitter, a social networking service that specializes in online news and information excha...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-04-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/11/9/4105 |
| _version_ | 1797535620654956544 |
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| author | Luis Daniel Samper-Escalante Octavio Loyola-González Raúl Monroy Miguel Angel Medina-Pérez |
| author_facet | Luis Daniel Samper-Escalante Octavio Loyola-González Raúl Monroy Miguel Angel Medina-Pérez |
| author_sort | Luis Daniel Samper-Escalante |
| collection | DOAJ |
| description | The reach and influence of social networks over modern society and its functioning have created new challenges and opportunities to prevent the misuse or tampering of such powerful tools of social interaction. Twitter, a social networking service that specializes in online news and information exchange involving billions of users world-wide, has been infested by bots for several years. In this paper, we analyze both public and private databases from the literature of bot detection on Twitter. We summarize their advantages, disadvantages, and differences, recommending which is more suitable to work with depending on the necessities of the researcher. From this analysis, we present five distinct behaviors in automated accounts exhibited across all the bot datasets analyzed from these databases. We measure their level of presence in each dataset using a radar chart for visual comparison. Finally, we identify four challenges that researchers of bot detection on Twitter have to face when using these databases from the literature. |
| first_indexed | 2024-03-10T11:48:00Z |
| format | Article |
| id | doaj.art-3e9d05d397a44c34ae013c71ee9aa633 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T11:48:00Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-3e9d05d397a44c34ae013c71ee9aa6332023-11-21T17:55:04ZengMDPI AGApplied Sciences2076-34172021-04-01119410510.3390/app11094105Bot Datasets on Twitter: Analysis and ChallengesLuis Daniel Samper-Escalante0Octavio Loyola-González1Raúl Monroy2Miguel Angel Medina-Pérez3School of Engineering and Sciences, Tecnologico de Monterrey, Puebla 72453, MexicoAltair Management Consultants Corp., 303 Wyman St., Suite 300, Waltham, MA 02451, USASchool of Engineering and Sciences, Tecnologico de Monterrey, Estado de Mexico 52926, MexicoSchool of Engineering and Sciences, Tecnologico de Monterrey, Estado de Mexico 52926, MexicoThe reach and influence of social networks over modern society and its functioning have created new challenges and opportunities to prevent the misuse or tampering of such powerful tools of social interaction. Twitter, a social networking service that specializes in online news and information exchange involving billions of users world-wide, has been infested by bots for several years. In this paper, we analyze both public and private databases from the literature of bot detection on Twitter. We summarize their advantages, disadvantages, and differences, recommending which is more suitable to work with depending on the necessities of the researcher. From this analysis, we present five distinct behaviors in automated accounts exhibited across all the bot datasets analyzed from these databases. We measure their level of presence in each dataset using a radar chart for visual comparison. Finally, we identify four challenges that researchers of bot detection on Twitter have to face when using these databases from the literature.https://www.mdpi.com/2076-3417/11/9/4105bot behaviorbot datasetstwitterdatabase analysisdatabase challenges |
| spellingShingle | Luis Daniel Samper-Escalante Octavio Loyola-González Raúl Monroy Miguel Angel Medina-Pérez Bot Datasets on Twitter: Analysis and Challenges Applied Sciences bot behavior bot datasets database analysis database challenges |
| title | Bot Datasets on Twitter: Analysis and Challenges |
| title_full | Bot Datasets on Twitter: Analysis and Challenges |
| title_fullStr | Bot Datasets on Twitter: Analysis and Challenges |
| title_full_unstemmed | Bot Datasets on Twitter: Analysis and Challenges |
| title_short | Bot Datasets on Twitter: Analysis and Challenges |
| title_sort | bot datasets on twitter analysis and challenges |
| topic | bot behavior bot datasets database analysis database challenges |
| url | https://www.mdpi.com/2076-3417/11/9/4105 |
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