A Congressional Twitter network dataset quantifying pairwise probability of influence
We present a social network dataset based on interactions between members of the 117th United States Congress between Feb. 9, 2022, and June 9, 2022. The dataset takes the form of a directed, weighted network in which the edge weights are empirically obtained “probabilities of influence” between all...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-10-01
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| Series: | Data in Brief |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340923006212 |
| _version_ | 1797660441812402176 |
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| author | Christian G. Fink Nathan Omodt Sydney Zinnecker Gina Sprint |
| author_facet | Christian G. Fink Nathan Omodt Sydney Zinnecker Gina Sprint |
| author_sort | Christian G. Fink |
| collection | DOAJ |
| description | We present a social network dataset based on interactions between members of the 117th United States Congress between Feb. 9, 2022, and June 9, 2022. The dataset takes the form of a directed, weighted network in which the edge weights are empirically obtained “probabilities of influence” between all pairs of Congresspeople. Twitter's application programming interface (API) V2 was used to determine the number of times each member of Congress retweeted, quote tweeted, replied to, or mentioned other Congressional members, and the probability of influence was found by normalizing the summed influence by the number of tweets issued by each Congressperson. This network may be of particular interest to the study of information diffusion within social networks. |
| first_indexed | 2024-03-11T18:30:54Z |
| format | Article |
| id | doaj.art-56fce2f9f8ee4020b8550e0d1b6f6946 |
| institution | Directory Open Access Journal |
| issn | 2352-3409 |
| language | English |
| last_indexed | 2024-03-11T18:30:54Z |
| publishDate | 2023-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj.art-56fce2f9f8ee4020b8550e0d1b6f69462023-10-13T11:04:55ZengElsevierData in Brief2352-34092023-10-0150109521A Congressional Twitter network dataset quantifying pairwise probability of influenceChristian G. Fink0Nathan Omodt1Sydney Zinnecker2Gina Sprint3Gonzaga University Physics Department, Gonzaga University, 502 E Boone Ave Spokane, WA 99258, USA; Corresponding author.Gonzaga University Mechanical Engineering Department, Gonzaga University, 502 E Boone Ave Spokane, WA 99258, USAGonzaga University Physics Department, Gonzaga University, 502 E Boone Ave Spokane, WA 99258, USAGonzaga University Computer Science Department, Gonzaga University, 502 E Boone Ave Spokane, WA 99258, USAWe present a social network dataset based on interactions between members of the 117th United States Congress between Feb. 9, 2022, and June 9, 2022. The dataset takes the form of a directed, weighted network in which the edge weights are empirically obtained “probabilities of influence” between all pairs of Congresspeople. Twitter's application programming interface (API) V2 was used to determine the number of times each member of Congress retweeted, quote tweeted, replied to, or mentioned other Congressional members, and the probability of influence was found by normalizing the summed influence by the number of tweets issued by each Congressperson. This network may be of particular interest to the study of information diffusion within social networks.http://www.sciencedirect.com/science/article/pii/S2352340923006212Social networkTwitter networkInformation diffusionIndependent Cascade ModelSusceptible-Infected-Recovered (SIR) model |
| spellingShingle | Christian G. Fink Nathan Omodt Sydney Zinnecker Gina Sprint A Congressional Twitter network dataset quantifying pairwise probability of influence Data in Brief Social network Twitter network Information diffusion Independent Cascade Model Susceptible-Infected-Recovered (SIR) model |
| title | A Congressional Twitter network dataset quantifying pairwise probability of influence |
| title_full | A Congressional Twitter network dataset quantifying pairwise probability of influence |
| title_fullStr | A Congressional Twitter network dataset quantifying pairwise probability of influence |
| title_full_unstemmed | A Congressional Twitter network dataset quantifying pairwise probability of influence |
| title_short | A Congressional Twitter network dataset quantifying pairwise probability of influence |
| title_sort | congressional twitter network dataset quantifying pairwise probability of influence |
| topic | Social network Twitter network Information diffusion Independent Cascade Model Susceptible-Infected-Recovered (SIR) model |
| url | http://www.sciencedirect.com/science/article/pii/S2352340923006212 |
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