{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T06:10:17Z","timestamp":1780380617862,"version":"3.54.1"},"reference-count":38,"publisher":"Emerald","issue":"1","license":[{"start":{"date-parts":[[2016,3,14]],"date-time":"2016-03-14T00:00:00Z","timestamp":1457913600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016,3,14]]},"abstract":"<jats:sec>\n               <jats:title content-type=\"abstract-heading\">Purpose<\/jats:title>\n               <jats:p> \u2013 This paper aims to research as to how Twitter is influential as an electronic word-of-mouth (e-WOM) communication tool and thereby affecting movie market. In present days, social media is playing an important role in connecting people around the globe. The technology has provided a platform in the social media space for people to share their experiences through text, photos and videos. Twitter is one such online social networking media that enables its users to send and read text-based messages of up to 140 characters, known as \u201ctweets\u201d. Twitter has nearly 200 million users and billions of such tweets are generated by users every other day. Social media micro-blogging broadcasting networks such as Twitter are transforming the way e-WOM is disseminated and consumed in the digital world. Twitter social behaviour for the Hollywood movies has been assessed across seven countries to validate the two basic blocks of the honeycomb model \u2013 sharing and conversation. Twitter behaviour was studied for 27 movies in 22 different cities of seven countries and for six genres with a total tweets of 9.28 million. The difference of Twitter social media behaviour was compared across countries, and \u201csharing\u201d and \u201cconversation\u201d as two building blocks of the honeycomb model were studied. <jats:italic>t<\/jats:italic>-Test results revealed that the behaviour is different across countries and across genres. <\/jats:p>\n            <\/jats:sec>\n            <jats:sec>\n               <jats:title content-type=\"abstract-heading\">Design\/methodology\/approach<\/jats:title>\n               <jats:p> \u2013 The objective of the paper is to analyse Twitter messages on an entertainment product (movies) across different regions of the world. Hollywood movies are released across different parts of the world, and Twitter users are also in different parts of the world. The objective is to hence validate \u201cconversation\u201d and \u201csharing\u201d building blocks of the honeycomb model. The research is confined to analysing Twitter data related to a few Hollywood movies. The tweets were collected across nine different cities spanning four different countries where English language is prominent. To understand the Twitter social media behaviour, a crawler application using Python and Java was developed to collect tweets of Hollywood movies from the Twitter database. The application has incorporated Twitter application programming interfaces (APIs) to access the Twitter database to extract tweets according to movies search queries across different parts of the world. The searching, collecting and analysing of the tweets is a rather challenging task because of various reasons. The tweets are stored in a Twitter corpus and can be accessed by the public using APIs. To understand whether tweets vary from one country to another, the analysis of variance test was conducted. To assess whether Twitter behaviour is different, and to compare the behaviour across countries, <jats:italic>t<\/jats:italic>-tests were conducted taking two countries at a time. The comparisons were made across all the six genres. In this way, an attempt was made to obtain a microscopic view of the Twitter behaviour for each of the seven countries and the six genres. <\/jats:p>\n            <\/jats:sec>\n            <jats:sec>\n               <jats:title content-type=\"abstract-heading\">Findings<\/jats:title>\n               <jats:p> \u2013 The findings show that the people use social media across the world. Nearly 9.28 million tweets were from seven countries, namely, USA, UK, Canada, South Africa, Australia, India and New Zealand for 27 Hollywood movies. This is indicative of the fact that today, people are exchanging information across different countries, that people are conversing about a product on social media and people are sharing information about a product on social media and, thus, proving the hypothesis. Further, the results indicate that the users in USA, Canada and UK, tweet more than the other countries, USA and UK being the highest in tweets followed by the Canada. On the other hand, the number of tweets in Australia, India and South Africa are low with New Zealand being the lowest of all the countries. This indicates that different countries\u2019 users have different social media behaviour. Some countries use social media to communicate about their experience more than in some other country. However, consumers from all over the world are using Twitter to express their views openly and freely. <\/jats:p>\n            <\/jats:sec>\n            <jats:sec>\n               <jats:title content-type=\"abstract-heading\">Originality\/value<\/jats:title>\n               <jats:p> \u2013 This research is useful to scholars and enterprises to understand opinions on Twitter social media and predict their impact. The study can be extended to any products which can lead to better customer relationship management. Companies can use the Internet and social media to promote and get feedback on their products and services across different parts of the world. Governments can inform the public about their new policies, benefits of governmental programmes to people and ways to improve the Internet reach to more people and also for creating awareness about health, hygiene, natural calamities and safety.<\/jats:p>\n            <\/jats:sec>","DOI":"10.1108\/jsit-12-2014-0074","type":"journal-article","created":{"date-parts":[[2016,3,7]],"date-time":"2016-03-07T04:00:08Z","timestamp":1457323208000},"page":"89-115","source":"Crossref","is-referenced-by-count":27,"title":["Understanding Twitter as an e-WOM"],"prefix":"10.1108","volume":"18","author":[{"given":"Umesh Rao","family":"Hodeghatta","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sangeeta","family":"Sahney","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"140","reference":[{"key":"key2020121800515703600_b1","doi-asserted-by":"crossref","unstructured":"Anderson, J.C.\n                (1995), \u201cRelationships in business markets: exchange episodes, value creation, and their empirical assessment\u201d, \n                  Journal of the Academy of Marketing Science\n               , Vol. 23 No. 4, pp. 346-350.","DOI":"10.1177\/009207039502300415"},{"key":"key2020121800515703600_b2","doi-asserted-by":"crossref","unstructured":"Anderson, K.M.\n                and \n                  Schram, A.\n                (2011), \u201cDesign and implementation of a data analytics infrastructure in support of crisis informatics research\u201d, Proceedings of the International Conference on Software Engineering, Waikiki, Honolulu, May, pp. 844-847.","DOI":"10.1145\/1985793.1985920"},{"key":"key2020121800515703600_b3","doi-asserted-by":"crossref","unstructured":"Asur, S.\n                and \n                  Huberman, B.A.\n                (2010), \u201cPredicting the future with social media\u201d, Proceedings of Web Intelligence and Intelligent Agent Technology (WI-IAT), Toronto, August, Vol. 1, pp. 492-499.","DOI":"10.1109\/WI-IAT.2010.63"},{"key":"key2020121800515703600_b4","doi-asserted-by":"crossref","unstructured":"Ba, S.\n                and \n                  Pavlou, P.A.\n                (2002), \u201cEvidences of the effect of trust in electronic markets: price premiums and buyer behavior\u201d, \n                  MIS Quarterly\n               , Vol. 26 No. 3, pp. 243-267.","DOI":"10.2307\/4132332"},{"key":"key2020121800515703600_b5","doi-asserted-by":"crossref","unstructured":"Bai, X.\n                (2011), \u201cPredicting consumer sentiments from online text\u201d, \n                  Decision Support Systems\n               , Vol. 50 No. 4, pp. 732-742.","DOI":"10.1016\/j.dss.2010.08.024"},{"key":"key2020121800515703600_b6","unstructured":"Beirut\n                (2009), \u201cWhy do people really tweet? 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