Demystifying Misconceptions in Social Bots Research
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866915414969155584 |
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| author | Cresci, Stefano Yang, Kai-Cheng Spognardi, Angelo Di Pietro, Roberto Menczer, Filippo Petrocchi, Marinella |
| author_facet | Cresci, Stefano Yang, Kai-Cheng Spognardi, Angelo Di Pietro, Roberto Menczer, Filippo Petrocchi, Marinella |
| contents | Research on social bots aims at advancing knowledge and providing solutions to one of the most debated forms of online manipulation. Yet, social bot research is plagued by widespread biases, hyped results, and misconceptions that set the stage for ambiguities, unrealistic expectations, and seemingly irreconcilable findings. Overcoming such issues is instrumental towards ensuring reliable solutions and reaffirming the validity of the scientific method. Here, we discuss a broad set of consequential methodological and conceptual issues that affect current social bots research, illustrating each with examples drawn from recent studies. More importantly, we demystify common misconceptions, addressing fundamental points on how social bots research is discussed. Our analysis surfaces the need to discuss research about online disinformation and manipulation in a rigorous, unbiased, and responsible way. This article bolsters such effort by identifying and refuting common fallacious arguments used by both proponents and opponents of social bots research, as well as providing directions toward sound methodologies for future research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2303_17251 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Demystifying Misconceptions in Social Bots Research Cresci, Stefano Yang, Kai-Cheng Spognardi, Angelo Di Pietro, Roberto Menczer, Filippo Petrocchi, Marinella Social and Information Networks Artificial Intelligence Computers and Society Machine Learning Research on social bots aims at advancing knowledge and providing solutions to one of the most debated forms of online manipulation. Yet, social bot research is plagued by widespread biases, hyped results, and misconceptions that set the stage for ambiguities, unrealistic expectations, and seemingly irreconcilable findings. Overcoming such issues is instrumental towards ensuring reliable solutions and reaffirming the validity of the scientific method. Here, we discuss a broad set of consequential methodological and conceptual issues that affect current social bots research, illustrating each with examples drawn from recent studies. More importantly, we demystify common misconceptions, addressing fundamental points on how social bots research is discussed. Our analysis surfaces the need to discuss research about online disinformation and manipulation in a rigorous, unbiased, and responsible way. This article bolsters such effort by identifying and refuting common fallacious arguments used by both proponents and opponents of social bots research, as well as providing directions toward sound methodologies for future research. |
| title | Demystifying Misconceptions in Social Bots Research |
| topic | Social and Information Networks Artificial Intelligence Computers and Society Machine Learning |
| url | https://arxiv.org/abs/2303.17251 |