Profiling Astroturfers on Facebook: A Complete Framework for Labeling, Feature Extraction, and Classification
The practice of online astroturfing has become increasingly pervasive in recent years, with the growth in popularity of social media. Astroturfing consists of promoting social, political, or other agendas in a non-transparent or deceitful way, where the promoters masquerade as normative users while...
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MDPI AG
2024-09-01
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| Series: | Machine Learning and Knowledge Extraction |
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| Online Access: | https://www.mdpi.com/2504-4990/6/4/108 |
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| author | Jonathan Schler Elisheva Bonchek-Dokow |
| author_facet | Jonathan Schler Elisheva Bonchek-Dokow |
| author_sort | Jonathan Schler |
| collection | DOAJ |
| description | The practice of online astroturfing has become increasingly pervasive in recent years, with the growth in popularity of social media. Astroturfing consists of promoting social, political, or other agendas in a non-transparent or deceitful way, where the promoters masquerade as normative users while acting behind a mask that conceals their true identity, and at times that they are not human. In politics, astroturfing is currently considered one of the most severe online threats to democracy. The ability to automatically identify astroturfers thus constitutes a first step in eradicating this threat. We present a complete framework for handling a dataset of profiles, from data collection and efficient labeling, through feature extraction, and finally, to the identification of astroturfers lurking in the dataset. The data were collected over a period of 15 months, during which three consecutive elections were held in Israel. These raw data are unique in scope and size, consisting of several million public comments and reactions to posts on political candidates’ pages. For the manual labeling stage, we present a technique that can zoom in on a sufficiently large subset of astroturfer profiles, thus making the procedure highly efficient. The feature extraction stage consists of a temporal layer of features, which proves useful for identifying astroturfers. We then applied and compared several algorithms in the classification stage, and achieved improved results, with an F<sub>1</sub> score of 77% and accuracy of 92%. |
| format | Article |
| id | doaj-art-e46caa80aa2b48b1a5810b68d9e74aaa |
| institution | Kabale University |
| issn | 2504-4990 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machine Learning and Knowledge Extraction |
| spelling | doaj-art-e46caa80aa2b48b1a5810b68d9e74aaa2024-12-27T14:37:26ZengMDPI AGMachine Learning and Knowledge Extraction2504-49902024-09-01642183220010.3390/make6040108Profiling Astroturfers on Facebook: A Complete Framework for Labeling, Feature Extraction, and ClassificationJonathan Schler0Elisheva Bonchek-Dokow1School of Computer Science, HIT (Holon Institute of Technology), Holon 5810201, IsraelSchool of Computer Science, HIT (Holon Institute of Technology), Holon 5810201, IsraelThe practice of online astroturfing has become increasingly pervasive in recent years, with the growth in popularity of social media. Astroturfing consists of promoting social, political, or other agendas in a non-transparent or deceitful way, where the promoters masquerade as normative users while acting behind a mask that conceals their true identity, and at times that they are not human. In politics, astroturfing is currently considered one of the most severe online threats to democracy. The ability to automatically identify astroturfers thus constitutes a first step in eradicating this threat. We present a complete framework for handling a dataset of profiles, from data collection and efficient labeling, through feature extraction, and finally, to the identification of astroturfers lurking in the dataset. The data were collected over a period of 15 months, during which three consecutive elections were held in Israel. These raw data are unique in scope and size, consisting of several million public comments and reactions to posts on political candidates’ pages. For the manual labeling stage, we present a technique that can zoom in on a sufficiently large subset of astroturfer profiles, thus making the procedure highly efficient. The feature extraction stage consists of a temporal layer of features, which proves useful for identifying astroturfers. We then applied and compared several algorithms in the classification stage, and achieved improved results, with an F<sub>1</sub> score of 77% and accuracy of 92%.https://www.mdpi.com/2504-4990/6/4/108astroturfingFacebookmachine learninglabelingfeature extraction |
| spellingShingle | Jonathan Schler Elisheva Bonchek-Dokow Profiling Astroturfers on Facebook: A Complete Framework for Labeling, Feature Extraction, and Classification Machine Learning and Knowledge Extraction astroturfing machine learning labeling feature extraction |
| title | Profiling Astroturfers on Facebook: A Complete Framework for Labeling, Feature Extraction, and Classification |
| title_full | Profiling Astroturfers on Facebook: A Complete Framework for Labeling, Feature Extraction, and Classification |
| title_fullStr | Profiling Astroturfers on Facebook: A Complete Framework for Labeling, Feature Extraction, and Classification |
| title_full_unstemmed | Profiling Astroturfers on Facebook: A Complete Framework for Labeling, Feature Extraction, and Classification |
| title_short | Profiling Astroturfers on Facebook: A Complete Framework for Labeling, Feature Extraction, and Classification |
| title_sort | profiling astroturfers on facebook a complete framework for labeling feature extraction and classification |
| topic | astroturfing machine learning labeling feature extraction |
| url | https://www.mdpi.com/2504-4990/6/4/108 |
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