Analisis Konten Twitter Dalam Kasus Boikot Sari Roti Paska Peristiwa Aksi Bela Islam 3
Abstract
Defending Islam 3 is a biggest mass action that happened on pra-reform era. A many event which accompanies that event after, which is interesting enough to be research further. One of event besides Defending Islam 3 event which have pretty much get the spotlight is a sari roti boycott after that event. It’s interesting because sari roti is a famous bread brand and have biggest market in Indonesia. Twitter is a one of platform sosial media which have largest user base in Indonesia. Twitter user in Indonesia. On this research, using simple data sampling and analysis to get linked tweet against sari roti boycott. Have registered, no less 40,000 tweet which was successfully collected within 6 days, from 22-27 December 2016.
Keywords: Social Media, Twitter, Sari Roti Boycott, Defending Islam 3, Text Analysis
ABSTRAK
Aksi Bela Islam 3 merupakan salah satu aksi masa terbesar yang terjadi pasca reformasi. Banyak sekali peristiwa yang mengiringi aksi tersebut yang cukup menarik untuk diteliti lebih lanjut. Salah satu peristiwa pendamping Aksi Bela Islam 3 yang cukup banyak mendapatkan sorotan adalah peristiwa pemboikotan produk Sari Roti yang terjadi pasca aksi. Hal ini menarik karena Sari Roti merupakan salah satu brand yang cukup terkenal dan memiliki pangsa pasar yang sangat luas di Indonesia. Twitter merupakan salah satu platform media sosial yang memiliki basis pengguna yang sangat besar di Indonesia. Dalam penelitian ini pengumpulan dan analisis sederhana terhadap tweet yang terkait dengan aksi boikot Sari Roti. Tercatat, tidak kurang dari 40 ribu tweet berhasil dikumpulkan dalam kurun waktu 6 hari, mulai dari tanggal 22 sampai 27 Desember 2016.
Kata kunci: Media Sosial, Twitter, Boikot Sari Roti, Aksi Bela Islam 3, Analisis Teks.
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References
Alamsyah, A., Paryasto, M., Putra, F. J., & Himmawan, R. (2016, May). Network text analysis to summarize online conversations for marketing intelligence efforts in telecommunication industry. In Information and Communication Technology (ICoICT), 2016 4th International Conference on (pp. 1-5). IEEE.
Arias, M., Arratia, A., & Xuriguera, R. (2013). Forecasting with twitter data. ACM Transactions on Intelligent Systems and Technology (TIST), 5(1), 8.
Aral, S., Dellarocas, C., & Godes, D. (2013). Introduction to the special issue—social media and business transformation: a framework for research. Information Systems Research, 24(1), 3-13.
Burgess, J., & Matamoros-Fernández, A. (2016). Mapping sociocultural controversies across digital media platforms: one week of# gamergate on Twitter, YouTube, and Tumblr. Communication Research and Practice, 2(1), 79-96.
Bollen, J., Mao, H., & Zeng, X. (2011). Twitter mood predicts the stock market. Journal Of Computational Science, 2(1), 1-8.
Dickey, M. (2014, January 10). Twitter Gears Up To Launch A Tweet Deck On Steroids For Journalists. Retrieved February 10, 2017, from http://www.businessinsider.com/twitter-and-dataminr-2014-1
Dragiewicz, M., & Burgess, J. (2016). Domestic violence on# qanda: The “Man†question in live Twitter discussion on the Australian Broadcasting Corporation's Q&A. Canadian journal of women and the law, 28(1), 211-229.
Feldman, R. (2013). Techniques and applications for sentiment analysis. Communications of the ACM, 56(4), 82-89.
Gayo-Avello, D. (2013). A meta-analysis of state-of-the-art electoral prediction from Twitter data. Social Science Computer Review, 31(6), 649-679.
Imtiyazi, M. A., Alamsyah, A., Junaedi, D., & Pradana, J. A. (2016, May). Word association network approach for summarizing Twitter conversation about public election. In Information and Communication Technology (ICoICT), 2016 4th International Conference on (pp. 1-4). IEEE.
Lasorsa, D. L., Lewis, S. C., & Holton, A. E. (2012). Normalizing Twitter: Journalism practice in an emerging communication space. Journalism studies, 13(1), 19-36.
Malhotra, A., Malhotra, C. K., & See, A. (2012). How to get your messages retweeted. MIT Sloan Management Review, 53(2), 61.
Park, H., Rodgers, S., & Stemmle, J. (2013). Analyzing health organizations' use of Twitter for promoting health literacy. Journal of health communication, 18(4), 410-425.
Takhteyev, Y., Gruzd, A., & Wellman, B. (2012). Geography of Twitter networks. Social networks, 34(1), 73-81.
Zappavigna, M. (2011). Ambient affiliation: A linguistic perspective on Twitter. New media & society, 13(5), 788-806.
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