BibTex RIS Kaynak Göster

LINGUISTIC STUDIES ON TWEETS GATHERED FROM MUĞLA REGION: A PRELIMINARY STUDY

Yıl 2016, Cilt: 2 Sayı: 2, 29 - 39, 19.10.2016

Öz

Citizens or visitors of a city can supply significant information with their social media posts by using mobile devices. These data can give information about complaints, touristic attractions, emergency situations etc. Social media analysis will be beneficial for smart city and smart management concept. This study is a first attempt to analyze and understand this touristic Muğla region by using social media. During this study, a sample dataset is formed by collecting the tweets that were sent from the Muğla region. Linguistic studies are implemented in tweets which are in Turkish language. Various techniques, statistical language and characteristics are used. Preliminary study revealed main topics about the region, user and hashtag types. We consider this analysis as a first step to a more detailed and complete study for this region.

Keywords: social media analysis, tweet, smart city, crowd sensing

Kaynakça

  • Anantharam, P., Barnaghi, P., Thirunarayan, K., and Sheth, A. (2015). Extracting city traffic events from social streams. ACM Transactions on Intelligent Systems and Technology (TIST), 6(4), 43.
  • Bakıcı, T., Almirall, E., and Wareham, J. (2013). A smart city initiative: the case of Barcelona. Journal of the Knowledge Economy, 4(2), 135-148.
  • Bowerman, B., et al. "The vision of a smart city." 2nd International Life Extension Technology Workshop, Paris. Vol. 28. 2000.
  • Cavazos-Rehg, P.A., Krauss, M.J., Sowles, S., Connolly, S., Rosas, C., Bharadwaj, M., and Bierut, L.J. (2016). A content analysis of depression-related tweets. Computers in Human Behavior, 54: 351-357.
  • Demirci, S. (2014). Emotion Analysis On Turkish Tweets. M.Sc. Thesis, Middle East Technical University, Ankara.
  • Friedman, W. (1922). The Index of Coincidence and Its Applications in Cryptography. Publication No. 22. Geneva IL: Riverbank Publications.
  • Ilina, E., Hauff, C., Celik, I., Abel, F., and Houben, G.J. (2012). Social event detection on twitter. In Web Engineering (pp. 169-176). Springer Berlin Heidelberg.
  • INFOSEC Institute, Crowdsensing: State of the Art and Privacy Aspects, http://resources.infosecinstitute.com/crowdsensing-state-art-privacy-aspects/, 2014.
  • Kit, C., and Wilks, Y. (1998). The Virtual Approach to Deriving Ngram Statistics from Large Scale Corpora. International Conference on Chinese Information Processing Conference, Beijing, China, pp. 223—229.
  • Kunneman, F., Hürriyetoğlu, A., Oostdijk, N., and van den Bosch, A. (2014). Timely identification of event start dates from Twitter. Computational Linguistics in the Netherlands Journal, 4: 39-52.
  • Leung, D., Law, R., Van Hoof, H., and Buhalis, D. (2013). Social media in tourism and hospitality: A literature review. Journal of Travel & Tourism Marketing, 30(1-2), 3-22.
  • Menezes, A., van Oorschot, P. and Vanstone, S. (1996). Handbook of Applied Cryptography. CRC Press.
  • Mohammad, S.M., and Kiritchenko, S. (2015). Using Hashtags to Capture Fine Emotion Categories From Tweets. Computational Intelligence, 31 (2): 301-326.
  • Örücü, F. (2009). Turkish Language Characteristics And Author Identification. M.Sc. Thesis, Dokuz Eylül University, İzmir.
  • Preece, A., Webberley, W., and Braines D. (2015). Tasking the tweeters: Obtaining actionable information from human sensors. SPIE Defense+ Security. International Society for Optics and Photonics.
  • Quercia, D., Schifanella, R., and Aiello, L.M. (2014). The shortest path to happiness: Recommending beautiful, quiet, and happy routes in the city. In Proceedings of the 25th ACM conference on Hypertext and social media (pp. 116-125). ACM.
  • Roitman, H., et al. Harnessing the crowds for smart city sensing. Proceedings of the 1st international workshop on Multimodal crowd sensing. ACM, 2012.
  • Serrano, E., Iglesias, C.A., and Garijo, M. (2015). A Survey of Twitter Rumor Spreading Simulations. ICCCI 2015: 7th International Conference, September 21-23 2015, Madrid, Spain, pp. 113-122.
  • Shendge, G.V., Pawar, M.R., Patil, N.D., Pawar, P.R., and Bagul, D.B. (2015). Real time Tweet analysis for event detection & reporting system for Earthquake. IRJET: International Research Journal of Engineering and Technology, 2 (6): 706-712.
  • Tumasjan, A., Sprenger, T.O., Sandner, P.G., and Welpe, I.M. (2010). Predicting elections with Twitter:What 140 characters reveal about political sentiment. 4th International AAAI Conference on Weblogs and Social Media, May 23-26 2010, Washington, DC, pp. 178–185.
  • Zanzotto, F.M., Pennacchiotti, M., and Tsioutsiouliklis, K. (2011). Linguistic redundancy in twitter. Proceedings of the Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics.
  • Zielinski, A., and Bügel, U. (2012). Multilingual analysis of twitter news in support of mass emergency events. Proceedings of the 9th International ISCRAM Conference, April 22-25 2012, Vancouver, Canada.
Yıl 2016, Cilt: 2 Sayı: 2, 29 - 39, 19.10.2016

Öz

Kaynakça

  • Anantharam, P., Barnaghi, P., Thirunarayan, K., and Sheth, A. (2015). Extracting city traffic events from social streams. ACM Transactions on Intelligent Systems and Technology (TIST), 6(4), 43.
  • Bakıcı, T., Almirall, E., and Wareham, J. (2013). A smart city initiative: the case of Barcelona. Journal of the Knowledge Economy, 4(2), 135-148.
  • Bowerman, B., et al. "The vision of a smart city." 2nd International Life Extension Technology Workshop, Paris. Vol. 28. 2000.
  • Cavazos-Rehg, P.A., Krauss, M.J., Sowles, S., Connolly, S., Rosas, C., Bharadwaj, M., and Bierut, L.J. (2016). A content analysis of depression-related tweets. Computers in Human Behavior, 54: 351-357.
  • Demirci, S. (2014). Emotion Analysis On Turkish Tweets. M.Sc. Thesis, Middle East Technical University, Ankara.
  • Friedman, W. (1922). The Index of Coincidence and Its Applications in Cryptography. Publication No. 22. Geneva IL: Riverbank Publications.
  • Ilina, E., Hauff, C., Celik, I., Abel, F., and Houben, G.J. (2012). Social event detection on twitter. In Web Engineering (pp. 169-176). Springer Berlin Heidelberg.
  • INFOSEC Institute, Crowdsensing: State of the Art and Privacy Aspects, http://resources.infosecinstitute.com/crowdsensing-state-art-privacy-aspects/, 2014.
  • Kit, C., and Wilks, Y. (1998). The Virtual Approach to Deriving Ngram Statistics from Large Scale Corpora. International Conference on Chinese Information Processing Conference, Beijing, China, pp. 223—229.
  • Kunneman, F., Hürriyetoğlu, A., Oostdijk, N., and van den Bosch, A. (2014). Timely identification of event start dates from Twitter. Computational Linguistics in the Netherlands Journal, 4: 39-52.
  • Leung, D., Law, R., Van Hoof, H., and Buhalis, D. (2013). Social media in tourism and hospitality: A literature review. Journal of Travel & Tourism Marketing, 30(1-2), 3-22.
  • Menezes, A., van Oorschot, P. and Vanstone, S. (1996). Handbook of Applied Cryptography. CRC Press.
  • Mohammad, S.M., and Kiritchenko, S. (2015). Using Hashtags to Capture Fine Emotion Categories From Tweets. Computational Intelligence, 31 (2): 301-326.
  • Örücü, F. (2009). Turkish Language Characteristics And Author Identification. M.Sc. Thesis, Dokuz Eylül University, İzmir.
  • Preece, A., Webberley, W., and Braines D. (2015). Tasking the tweeters: Obtaining actionable information from human sensors. SPIE Defense+ Security. International Society for Optics and Photonics.
  • Quercia, D., Schifanella, R., and Aiello, L.M. (2014). The shortest path to happiness: Recommending beautiful, quiet, and happy routes in the city. In Proceedings of the 25th ACM conference on Hypertext and social media (pp. 116-125). ACM.
  • Roitman, H., et al. Harnessing the crowds for smart city sensing. Proceedings of the 1st international workshop on Multimodal crowd sensing. ACM, 2012.
  • Serrano, E., Iglesias, C.A., and Garijo, M. (2015). A Survey of Twitter Rumor Spreading Simulations. ICCCI 2015: 7th International Conference, September 21-23 2015, Madrid, Spain, pp. 113-122.
  • Shendge, G.V., Pawar, M.R., Patil, N.D., Pawar, P.R., and Bagul, D.B. (2015). Real time Tweet analysis for event detection & reporting system for Earthquake. IRJET: International Research Journal of Engineering and Technology, 2 (6): 706-712.
  • Tumasjan, A., Sprenger, T.O., Sandner, P.G., and Welpe, I.M. (2010). Predicting elections with Twitter:What 140 characters reveal about political sentiment. 4th International AAAI Conference on Weblogs and Social Media, May 23-26 2010, Washington, DC, pp. 178–185.
  • Zanzotto, F.M., Pennacchiotti, M., and Tsioutsiouliklis, K. (2011). Linguistic redundancy in twitter. Proceedings of the Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics.
  • Zielinski, A., and Bügel, U. (2012). Multilingual analysis of twitter news in support of mass emergency events. Proceedings of the 9th International ISCRAM Conference, April 22-25 2012, Vancouver, Canada.
Toplam 22 adet kaynakça vardır.

Ayrıntılar

Bölüm Makaleler
Yazarlar

Feriştah Dalkılıç

Enis Karaarslan

Ali Hürriyetoğlu Bu kişi benim

Enis Karaarslan

Ali Hürriyetoğlu Bu kişi benim

Yayımlanma Tarihi 19 Ekim 2016
Yayımlandığı Sayı Yıl 2016 Cilt: 2 Sayı: 2

Kaynak Göster

APA Dalkılıç, F., Karaarslan, E., Hürriyetoğlu, A., Karaarslan, E., vd. (2016). LINGUISTIC STUDIES ON TWEETS GATHERED FROM MUĞLA REGION: A PRELIMINARY STUDY. Yönetim Bilişim Sistemleri Dergisi, 2(2), 29-39.