The Use and Effect of Statistics in U.S. Professional Sports Leagues’ X Posts on Engagement, Enjoyment, and Emotion

IF 3.2 1区文学 Q1 COMMUNICATION Communication & Sport Pub Date : 2024-11-14 DOI:10.1177/21674795241299026

Dustin Hahn

{"title":"The Use and Effect of Statistics in U.S. Professional Sports Leagues’ X Posts on Engagement, Enjoyment, and Emotion","authors":"Dustin Hahn","doi":"10.1177/21674795241299026","DOIUrl":null,"url":null,"abstract":"This study examines the use and effect of statistics in online social media posts on X (formerly Twitter) for the top five professional sports leagues in the U.S. (NFL, NBA, MLB, MLS, and NHL) during 2023 for changes in engagement, enjoyment, and emotion. This study utilizes machine learning to code 49,455 X posts before employing AI-powered sentiment and emotion analysis tools, in conjunction with more traditional measures of engagement and enjoyment, of 136,401 mentions responding to a randomly sampled subset of 500 of these posts (50 with statistics and 50 without statistics present in each of the five leagues). First, findings revealed discrepancies in frequency of use of statistics across leagues. Next, while posts with statistics increased engagement, they also negatively impacted enjoyment. Finally, analysis revealed posts with statistics yielded more “sad” responses compared to more “joyful” responses to posts without statistics. However, results varied by sports league. Implications for exemplification theory and future sport communication research on the use of statistics in sports media and practical considerations for sports media professionals are discussed.","PeriodicalId":46882,"journal":{"name":"Communication & Sport","volume":"25 1","pages":""},"PeriodicalIF":3.2000,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Communication & Sport","FirstCategoryId":"98","ListUrlMain":"https://doi.org/10.1177/21674795241299026","RegionNum":1,"RegionCategory":"文学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMMUNICATION","Score":null,"Total":0}

引用次数: 0

Abstract

This study examines the use and effect of statistics in online social media posts on X (formerly Twitter) for the top five professional sports leagues in the U.S. (NFL, NBA, MLB, MLS, and NHL) during 2023 for changes in engagement, enjoyment, and emotion. This study utilizes machine learning to code 49,455 X posts before employing AI-powered sentiment and emotion analysis tools, in conjunction with more traditional measures of engagement and enjoyment, of 136,401 mentions responding to a randomly sampled subset of 500 of these posts (50 with statistics and 50 without statistics present in each of the five leagues). First, findings revealed discrepancies in frequency of use of statistics across leagues. Next, while posts with statistics increased engagement, they also negatively impacted enjoyment. Finally, analysis revealed posts with statistics yielded more “sad” responses compared to more “joyful” responses to posts without statistics. However, results varied by sports league. Implications for exemplification theory and future sport communication research on the use of statistics in sports media and practical considerations for sports media professionals are discussed.

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

美国职业体育联盟 X 帖子中统计数据的使用及其对参与、享受和情感的影响

本研究探讨了 2023 年美国五大职业体育联盟（NFL、NBA、MLB、MLS 和 NHL）在 X（原 Twitter）上发布的在线社交媒体帖子中使用统计数据的情况及其影响，以了解参与度、欣赏度和情感方面的变化。本研究利用机器学习对 49,455 X 个帖子进行编码，然后采用人工智能驱动的情感和情绪分析工具，结合更传统的参与度和欣赏度衡量方法，对 136,401 个提及进行了分析，并随机抽取了其中 500 个帖子的子集（五大联赛各 50 个有统计数据，50 个无统计数据）。首先，研究结果显示，不同联赛使用统计数据的频率存在差异。其次，虽然带有统计数据的帖子提高了参与度，但它们也对乐趣产生了负面影响。最后，分析表明，与没有统计数据的帖子相比，有统计数据的帖子产生了更多 "悲伤 "的回应，而没有统计数据的帖子则产生了更多 "快乐 "的回应。不过，不同体育联盟的结果也不尽相同。本研究讨论了在体育媒体中使用统计数据对示范理论和未来体育传播研究的影响，以及体育媒体专业人员的实际考虑因素。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文去求助

来源期刊

Communication & Sport Multiple-

CiteScore

7.00

自引率

11.10%

发文量