Decoding the News Media Diet of Disinformation Spreaders
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866911837602185216 |
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| author | Bertani, Anna Mazzeo, Valeria Gallotti, Riccardo |
| author_facet | Bertani, Anna Mazzeo, Valeria Gallotti, Riccardo |
| contents | In the digital era, information consumption is predominantly channeled through online news media disseminated on social media platforms. Understanding the complex dynamics of the news media environment and users habits within the digital ecosystem is a challenging task that requires at the same time large bases of data and accurate methodological approaches. This study contributes to this expanding research landscape by employing network science methodologies and entropic measures to analyze the behavioural patterns of social media users sharing news pieces and dig into the diverse news consumption habits within different online social media user groups. Our analyses reveal that users are more inclined to share news classified as fake when they have previously posted conspiracy or junk science content, and vice versa, creating a series of misinformation hot streaks. To better understand these dynamics, we used three different measures of entropy to gain insights into the news media habits of each user, finding that the patterns of news consumption significantly differ among users when focusing on disinformation spreaders, as opposed to accounts sharing reliable or low-risk content. Thanks to these entropic measures, we quantify the variety and the regularity of the news media diet, finding that those disseminating unreliable content exhibit a more varied and at the same time a more regular choice of web domains. This quantitative insight into the nuances of news consumption behaviours exhibited by disinformation spreaders holds the potential to significantly inform the strategic formulation of more robust and adaptive social media moderation policies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_02173 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Decoding the News Media Diet of Disinformation Spreaders Bertani, Anna Mazzeo, Valeria Gallotti, Riccardo Physics and Society Social and Information Networks In the digital era, information consumption is predominantly channeled through online news media disseminated on social media platforms. Understanding the complex dynamics of the news media environment and users habits within the digital ecosystem is a challenging task that requires at the same time large bases of data and accurate methodological approaches. This study contributes to this expanding research landscape by employing network science methodologies and entropic measures to analyze the behavioural patterns of social media users sharing news pieces and dig into the diverse news consumption habits within different online social media user groups. Our analyses reveal that users are more inclined to share news classified as fake when they have previously posted conspiracy or junk science content, and vice versa, creating a series of misinformation hot streaks. To better understand these dynamics, we used three different measures of entropy to gain insights into the news media habits of each user, finding that the patterns of news consumption significantly differ among users when focusing on disinformation spreaders, as opposed to accounts sharing reliable or low-risk content. Thanks to these entropic measures, we quantify the variety and the regularity of the news media diet, finding that those disseminating unreliable content exhibit a more varied and at the same time a more regular choice of web domains. This quantitative insight into the nuances of news consumption behaviours exhibited by disinformation spreaders holds the potential to significantly inform the strategic formulation of more robust and adaptive social media moderation policies. |
| title | Decoding the News Media Diet of Disinformation Spreaders |
| topic | Physics and Society Social and Information Networks |
| url | https://arxiv.org/abs/2402.02173 |