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An Indirected Recommendation Model for Chinese Microblog

References 1. Sun, L., Y. Liu, Q.-A. Zeng, F. Xiong. A Novel Rumor Diffusion Model Considering the Effect of Truth in Online Social Media. - International Journal of Modern Physics, Vol. 26, 2015, No 7, pp. 1-20. 2. He, Y., J. Tan. Study on Sina Micro-Blog Personalized Recommendation Based on Semantic Network. - Expert Systems with Applications, Vol. 42, 2015, pp. 4797-4804. 3. Zhou, X., S. Wua, C. Chen, G. Chen. Real-Time Recommendation for Microblogs. - Information Sciences, Vol. 279, 2014, pp. 301

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An Influence Prediction Model for Microblog Entries on Public Health Emergencies

1 Introduction Public health emergencies generally pose serious threats and significant losses to public health, economic development, and social stability. Since they break out in a short time and spread rapidly, the management departments of public health emergencies often face enormous challenges. They need to take effective measures in time to prevent the spread and upgrade of events, as well as eliminate the source of hazards and subsequent influence promptly. With the continuous development of Web 2.0 and mobile Internet technology, microblog

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Twitter usage in Tourism: Literature Review

. Provenzano, D., Hawelka, B., Baggio, R. (2018), “The mobility network of European tourists: a longitudinal study and a comparison with geo-located Twitter data”, Tourism Review, Vol. 73, No.1, Vol. 28-43. 33. Raamkumar, A. S., Pang, N., Foo, S. (2016), “When countries become the talking point in microblogs: Study on country hashtags in Twitter”, First Monday,Vol. 21, No. 1. 34. Rodriguez, N. S. (2017), “# FIFAputos: A Twitter Textual Analysis Over “Puto” at the 2014 World Cup”, Communication & Sport, Vol. 5, No. 6, pp. 712-731. 35. Rzeszewski, M. (2015

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Social Media as a Marketing Tool for European and North American Universities and Colleges


Objective: The purpose of the following study is to examine the approach to social media of European and North American higher education institutions ranked in the Top100 on the 2017 Academic Ranking of World Universities (ARWU). Data regarding the number of publications and the number of followers of each social media were analysed.

Methodology: The present study is quantitative in nature. The sample consisted of the European and North American universities and colleges listed in the Top 100 of the ARWU 2017: in total, 48 institutions in the United States and 35 in Europe were identified. To analyse the official social media sites used by each higher education institution, the links presented on the Homepage of the universities’ website were followed. Data was collected between the 27nd of August and the 2nd of September 2018. Two different types of variable groups were defined: 1) the number and type of Universities’ publications, and 2) the number of followers on each social media. For benefit of the research the authors considered Facebook, LinkedIn, Google+, Weibo and VKontakte as social networking sites; Instagram, Pinterest, Flickr and Snapchat, as photo sharing platforms; Youtube, and Vimeo as video sharing platforms, and finally Twitter and Tumblr as microblogs.

Findings: European and North American universities and colleges invest in marketing activities in social media. Regarding the number of social networking sites, content sharing and microblogging platforms no significant differences were found between means of the two independent samples. The most popular social media used are Facebook and Twitter ex-aequo, followed by Youtube, Instagram and LinkedIn. Concerning the number of publications on these media, significant differences by region are present for the variable number of photos and videos on Facebook, number of Instagram posts, and tweets. Furthermore, on all the prominent social media, North American universities and colleges benefit from a substantial higher number of followers than their counterpart. European users favour Facebook, LinkedIn, Twitter, and only then Instagram. Participation in G+ is marginal. In the United States the preferred social media are Facebook, LinkedIn, G+, Twitter, and Instagram. Regarding user engagement, measured by the number of followers, equality of means between the two independent samples were found for Facebook, Pinterest, Flickr and Youtube. Differences exist for the social media: LinkedIn, G+, Instagram, and Twitter. G+ is quite popular in the United States, but not in Europe, and Twitter attracts visibly more followers too.

Value Added: The contribution of this research paper consists in better understanding, from a quantitative point of view, differences between the use of social media as a marketing tool by the European and North American higher education institutions listed in the Top100 of the ARWU 2017. Regional differences exist, even though universities and colleges compete on a worldwide basis.

Recommendations: From an academic perspective, a qualitative study approach is advised to better understand the concurrence of the number of publications and followers on the different social media, since significant Pearson correlations between variables were identified. As practical implications, marketers from the European higher education institutions should invest more in posts, uploads and tweets. For both regions, the social networking site LinkedIn has been neglected, despite the high number of followers.

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Can Social Media Content Increase Financial Market Returns? Survey Results from Poland

September 19, 2016, from Sprenger, T.O., & Welpe, I.M. (2010). Tweets and trades: The information content of stock microblogs. Retrieved September 19, 2016, from Tetlock, P.C. (2007). Giving content to investor sentiment: The role of media in the stock market. Journal of Finance 62(3), 1139-1168, Tetlock, P.C., Saar-Tsechansky, M., & Macskassy, S. (2008). More than

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A survey on sentiment classification algorithms, challenges and applications

Physical Journal (2016) 76 : 107. ⇒ 67 [4] R. N. Behera, R. Manan, S. Dash, Ensemble based hybrid machine learning approach for sentiment classification – A Review, International Journal of Computer Applications 146 , 6 (2016) 31–36. ⇒ 59 [5] S. Brody, N. Diakopoulos, Cooooooooooooooollllllllllllll!!!!!!!!!!!!!!: using word lengthening to detect sentiment in microblogs, Conference on Empirical Methods in Natural Language Processing , 2007, pp. 562–570. ⇒ 64 [6] Y. Choi, H.Lee, Data properties and the performance of sentiment classification for

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Information needs of financial market professionals in the big data and social media era. The empirical evidence from Poland

on 10/09/2016). Sprenger, T.O., Welpe, I.M. (2010). Tweets and Trades: The Information Content of Stock Microblogs. Working paper. Retrieved from (referred on 12/01/2017). Turner, D., Shroeck, M., Shockley, R. (2013). Analytics: The Real-World Use of Big Data in Financial Services. Retrieved from (referred on 10/09/2016).

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Memetic pool as a new approach in service quality analysis

.sbspro.2015.01.1187 Tosun, C., Dedeoğlu, B. B., & Fyall, A. (2015). Destination service quality, affective image and revisit intention: The moderating role of past experience. Journal of Destination Marketing & Management , 4 (4), 222-234. doi: 10.1016/j.jdmm.2015.08.002 Urban, W. (2007). Definicje jakości usług – różnice oraz ich przyczyny [Definitions of service quality – differences and their reasons]. Problemy Jakości , 3 , 4-9. Xu, H., Yang, W., & Wang, J. (2015). Hierarchical emotion classification and emotion component analysis on Chinese micro-blog

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Hybrid Recommender System via Personalized Users’ Context

Factorization for Document Context-Aware Recommendation. – In: Proc. of 10th ACM Conf. Recomm. Syst. (RecSys’16), 2016, pp. 233-240. 18. Yu, P., L. Lin, J. Wang. A Novel Framework to Alleviate the Sparsity Problem in Context-Aware Recommender Systems. – New Rev. Hypermedia Multimed., Vol. 23 , April 2017, No 2, pp. 141-158. 19. Deng, S., D. Wang, X. Li, G. Xu. Exploring User Emotion in Microblogs for Music Recommendation. – Expert Syst. Appl., Vol. 42 , 2015, No 23, pp. 9284-9293. 20. Zhao, Z., et al. Social-Aware Movie Recommendation via Multimodal

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