{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,27]],"date-time":"2025-08-27T15:49:50Z","timestamp":1756309790468,"version":"3.41.2"},"reference-count":80,"publisher":"Emerald","issue":"2","license":[{"start":{"date-parts":[[2019,7,24]],"date-time":"2019-07-24T00:00:00Z","timestamp":1563926400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["INTR"],"published-print":{"date-parts":[[2019,7,24]]},"abstract":"<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title>\n<jats:p>The purpose of this paper is to explore the interrelationship between television (TV) consumption (viewing ratings), engagement behaviors of different actors on Twitter (TV programs, media, celebrities and viewers) and the content of engagement behaviors (affective, program-related and social content).<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title>\n<jats:p>TV ratings and Twitter data were obtained. The content of tweets was analyzed by means of a sentiment analysis. A vector auto regression model was used to understand the interrelationship between tweets of different actors and TV consumption.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Findings<\/jats:title>\n<jats:p>First, the results showed a negative interrelationship between TV viewing and viewers\u2019 tweeting behavior. Second, tweets by celebrities and media exhibited similar patterns and were both affected mostly by the number of tweets by viewers. Finally, the content of tweets matters. Affective tweets positively relate to TV viewing, and program-related and social content positively relates to the number of tweets by viewers.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Research limitations\/implications<\/jats:title>\n<jats:p>The findings help us understand the online engagement ecosystem and provide insights into drivers of TV consumption and online engagement of different actors.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Practical implications<\/jats:title>\n<jats:p>The results indicate that content producers may want to focus on stimulating affective conversations on Twitter to trigger more online and offline engagement. The results also call for rethinking the meaning of TV metrics.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title>\n<jats:p>While some studies have explored viewer interactions on Twitter, only a few studies have looked at the effects of such interactions on variables outside of social media, such as TV consumption. Moreover, the authors study the interrelations between Twitter interactions with TV consumption, which allows us to examine the effect of online engagement on offline behaviors and vice versa. Finally, the authors take different actors into account when studying real-life online engagement.<\/jats:p>\n<\/jats:sec>","DOI":"10.1108\/intr-08-2018-0389","type":"journal-article","created":{"date-parts":[[2019,8,6]],"date-time":"2019-08-06T10:46:24Z","timestamp":1565088384000},"page":"381-401","source":"Crossref","is-referenced-by-count":11,"title":["Engaging with TV events on Twitter"],"prefix":"10.1108","volume":"30","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2424-5737","authenticated-orcid":false,"given":"Claire Monique","family":"Segijn","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ewa","family":"Maslowska","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Theo","family":"Araujo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vijay","family":"Viswanathan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"140","reference":[{"issue":"2","key":"key2020071607310194400_ref001","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1080\/00913367.2015.1130665","article-title":"Don\u2019t distract me when I\u2019m media multitasking: toward a theory for raising advertising recall and recognition","volume":"45","year":"2016","journal-title":"Journal of Advertising"},{"issue":"2","key":"key2020071607310194400_ref002","doi-asserted-by":"crossref","first-page":"419","DOI":"10.1108\/IntR-04-2017-0172","article-title":"Communicating effectively about CSR on Twitter: the power of engaging strategies and storytelling elements","volume":"28","year":"2018","journal-title":"Internet Research"},{"issue":"3","key":"key2020071607310194400_ref003","doi-asserted-by":"crossref","first-page":"496","DOI":"10.1080\/02650487.2016.1173765","article-title":"Getting the word out on Twitter: the role of influentials, information brokers and strong ties in building word-of-mouth for brands","volume":"36","year":"2017","journal-title":"International Journal of Advertising"},{"issue":"1","key":"key2020071607310194400_ref004","first-page":"57","article-title":"Effectiveness of celebrity endorsers","volume":"23","year":"1983","journal-title":"Journal of Advertising Research"},{"issue":"2","key":"key2020071607310194400_ref005","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1086\/519142","article-title":"Where consumers diverge from others: identity signaling and product domains","volume":"34","year":"2007","journal-title":"Journal of Consumer Research"},{"issue":"2","key":"key2020071607310194400_ref006","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1509\/jmr.10.0353","article-title":"What makes online content viral?","volume":"49","year":"2012","journal-title":"Journal of Marketing Research"},{"issue":"3","key":"key2020071607310194400_ref007","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1177\/1094670511411703","article-title":"Customer engagement: conceptual domain, fundamental propositions, and implications for research","volume":"14","year":"2011","journal-title":"Journal of Service Research"},{"issue":"1","key":"key2020071607310194400_ref008","first-page":"39","article-title":"How to capture consumer experiences: a context-specific approach to measuring engagement","volume":"56","year":"2015","journal-title":"Journal of Advertising Research"},{"year":"2010","key":"key2020071607310194400_ref009","article-title":"Measuring user influence in Twitter: the million follower fallacy"},{"key":"key2020071607310194400_ref010","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/j.chb.2014.11.087","article-title":"The effect of multitasking on time perception, enjoyment, and ad evaluation","volume":"45","year":"2015","journal-title":"Computers in Human Behavior"},{"first-page":"79","article-title":"Who is on your sofa? 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