مولفه های اصلی ارزیابی اعتبار کاربران با توجه به اهداف سازمانی در چرخۀ حیات کلان داده
محورهای موضوعی : فناوری اطلاعات و ارتباطات
سوگند دهقان
1
,
شهریار محمدی
2
*
,
روجیار پیرمحمدیانی
3
1 - دانشکده مهندسی صنایع، دانشگاه صنعتی خواجه نصیرالدین طوسی، ایران
2 - دانشکده مهندسی صنایع، دانشگاه صنعتی خواجه نصیرالدین طوسی، ایران
3 - عضو هیئت علمی دانشگاه کردستان
کلید واژه: اعتبار اطلاعات, کاربران معتبر, منابع اطلاعاتي معتبر, چرخه حیات کلان داده, اهداف سازماني, شبکههاي اجتماعي,
چکیده مقاله :
شبکههاي اجتماعي بهدلیل سرعت انتشار رویدادها و نیز حجم زیاد اطلاعات، به یکي از مهمترین منابع تصمیمگیري در سازمانها تبدیل شدهاند. ولي پیش از استفاده، صحت، قابلیت اطمینان و ارزش اطلاعات تولید شده توسط آنها، باید مورد ارزیابي قرار گیرد. به این منظور، بررسي اعتبار اطلاعات با توجه به ویژگيهاي شبکههاي اجتماعي در سه سطح کاربر، محتوا و رویداد امکانپذیر ميباشد. سطح کاربر، قابل اطمینانترین سطح این حوزه ميباشد، زیرا کاربر معتبر، معمولا اقدام به انتشار محتواي معتبر مينماید. از این رو، ارزیابي سطح کاربر مورد توجه این پژوهش ميباشد. بیشتر مقالات مرتبط به حوزۀ ارزیابي اعتبار کاربران شبکههاي اجتماعي به ارزیابي اعتبار کاربران در حالت کلي پرداختهاند و از اهداف سازماني مانند ارزیابي اعتبار کاربران به منظور یافتن افراد خلاق چشم پوشي نمودهاند. همچنین چرخه حیات کلان داده و مولفههاي مهم در فرآیند ارزیابي اعتبار کاربران کمتر مورد توجه قرار گرفتهاند. از این رو، این تحقیق با بررسي 50 مقاله مهم در این حوزه، مولفههاي مهم را به سه مولفه اصلي )تعیین موضوع محتوا، انتخاب ویژگيها و ارزیابي اعتبار( دسته بندي مينماید و روشها و ویژگيهاي مربوط به هر یک را مورد بحث قرار ميدهد. نهایتا یک چارچوب اولیه ارزیابي اعتبار کاربران شبکههاي اجتماعي با توجه به اهداف سازماني و چرخه حیات کلان داده ارائه گردید. هدف این چارچوب، ارائه یک راهنما مناسب به سازمانها، براي محاسبۀ میزان اعتبار کاربران در فرآیند تصمیمگیري ميباشد.
Social networks have become one of the most important decision-making factors in organizations due to the speed of publishing events and the large amount of information. For this reason, they are one of the most important factors in the decision-making process of information validity. The accuracy, reliability and value of the information are clarified by these networks. For this purpose, it is possible to check the validity of information with the features of these networks at the three levels of user, content and event. Checking the user level is the most reliable level in this field, because a valid user usually publishes valid content. Despite the importance of this topic and the various researches conducted in this field, important components in the process of evaluating the validity of social network information have received less attention. Hence, this research identifies, collects and examines the related components with the narrative method that it does on 30 important and original articles in this field. Usually, the articles in this field are comparable from three dimensions to the description of credit analysis approaches, content topic detection, feature selection methods. Therefore, these dimensions have been investigated and divided. In the end, an initial framework was presented focusing on evaluating the credibility of users as information sources. This article is a suitable guide for calculating the amount of credit of users in the decision-making process.
1] Pasi, G., Viviani, M. and Carton, A., 2019. A Multi-Criteria Decision Making approach based on the Choquet integral for assessing the credibility of User-Generated Content. Information Sciences, 503, pp.574-588. #
[2] Tabassum, S., Pereira, F.S., Fernandes, S. and Gama, J., 2018. Social network analysis: An overview. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(5), p.e1256. #
[3] Erl, T., Khattak, W. and Buhler, P., 2016. Big data fundamentals: concepts, drivers & techniques (Vol. 1). Boston: Prentice Hall. #
]4[ روجیار پیرمحمدیانی ، شهریار محمدی ، معیارهای ارزیابی ارزش اثرگذاری کاربران رسانه های اجتماعی چارچوبی براساس کاوش رسانه های اجتماعی ، فناوری اطلاعات و ارتباطات ایران ، سال یازدهم شماره ی 39 و 40 ، صفحه 109-125،1398 #
[5] Abu-Salih, B., Bremie, B., Wongthongtham, P., Duan, K., Issa, T., Chan, K.Y., Alhabashneh, M., Albtoush, T., Alqahtani, S., Alqahtani, A. and Alahmari, M., 2019, March. Social credibility incorporating semantic analysis and machine learning: a survey of the state-of-the-art and future research directions. In Workshops of the International Conference on Advanced Information Networking and Applications (pp. 887-896). Springer, Cham. #
[6] Alrubaian, M., Al-Qurishi, M., Alamri, A., Al-Rakhami, M., Hassan, M.M. and Fortino, G., 2018. Credibility in online social networks: A survey. IEEE Access, 7, pp.2828-2855. #
[7] Abbasimehr, H., Nourani, E. and Shabani, M., 2020. A hybrid framework for ranking reviewers based on interval type-2 fuzzy AHP and VIKOR. International Journal of Intelligent Engineering Informatics, 8(2), pp.95-116. #
[8] Morris, M.R., Counts, S., Roseway, A., Hoff, A. and Schwarz, J., 2012, February. Tweeting is believing? Understanding microblog credibility perceptions. In Proceedings of the ACM 2012 conference on computer supported cooperative work (pp. 441-450). #
[9] Al-Garadi, M.A., Varathan, K.D., Ravana, S.D., Ahmed, E., Mujtaba, G., Khan, M.U.S. and Khan, S.U., 2018. Analysis of online social network connections for identification of influential users: Survey and open research issues. ACM Computing Surveys (CSUR), 51(1), pp.1-37. #
[10] Keller, J., Wong, S.S. and Liou, S., 2020. How social networks facilitate collective responses to organizational paradoxes. Human Relations, 73(3), pp.401-428. #
[11]Olszak, C.M., Bartuś, T. and Lorek, P., 2018. A comprehensive framework of information system design to provide organizational creativity support. Information & Management, 55(1), pp.94-108. #
[12] Siciliano, M.D. and Thompson, J.R., 2018. If you are committed, then so am I: The role of social networks and social influence on organizational commitment. Administration & Society, 50(7), pp.916-946. #
[13] Pasi, G., De Grandis, M. and Viviani, M., 2020, July. Decision making over multiple criteria to assess news credibility in microblogging sites. In 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (pp. 1-8). IEEE. #
[14] Evans, L., Owda, M., Crockett, K. and Vilas, A.F., 2021. Credibility assessment of financial stock tweets. Expert Systems with Applications, 168, p.114351. #
[15] Abu-Salih, B., Wongthongtham, P., Chan, K.Y. and Zhu, D., 2019. CredSaT: Credibility ranking of users in big social data incorporating semantic analysis and temporal factor. Journal of Information Science, 45(2), pp.259-280. #
[16] Weerkamp, W. and de Rijke, M., 2012. Credibility-inspired ranking for blog post retrieval. Information retrieval, 15(3-4), pp.243-277. #
[17] Peng, S., Zhou, Y., Cao, L., Yu, S., Niu, J. and Jia, W., 2018. Influence analysis in social networks: A survey. Journal of Network and Computer Applications, 106, pp.17-32. #
[18] Afify, E., Sharaf Eldin, A., E Khedr, A. and Kamal Alsheref, F., 2019. User-generated content (UGC) credibility on social media using sentiment classification. النشرة المعلوماتیة فی الحاسبات والمعلومات, 1(1), pp.1-19. #
[19] Boididou, C., Papadopoulos, S., Zampoglou, M., Apostolidis, L., Papadopoulou, O. and Kompatsiaris, Y., 2018. Detection and visualization of misleading content on Twitter. International Journal of Multimedia Information Retrieval, 7(1), pp.71-86. #
[20] Hershkovitz, A. and Hayat, Z., 2020. The role of tie strength in assessing credibility of scientific content on facebook. Technology in Society, 61, p.101261 #
[21] Maes, F., Peters, S., Denoyer, L. and Gallinari, P., 2009, September. Simulated iterative classification a new learning procedure for graph labeling. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases (pp. 47-62). Springer, Berlin, Heidelberg. #
[22] Saikaew, K.R. and Noyunsan, C., 2015. Features for measuring credibility on facebook information. International Scholarly and Scientific Research & Innovation, 9(1), pp.174-177. #
[23] Alrubaian, M., Al‐Qurishi, M., Al‐Rakhami, M., Hassan, M.M. and Alamri, A., 2017. Reputation‐based credibility analysis of Twitter social network users. Concurrency and Computation: Practice and Experience, 29(7), p.e3873. #
[24] Arora, A., Bansal, S., Kandpal, C., Aswani, R. and Dwivedi, Y., 2019. Measuring social media influencer index-insights from Facebook, Twitter and Instagram. Journal of Retailing and Consumer Services, 49, pp.86-101. #
[25] bin Baharudin, B. and bin Md Said, A., 2018. Trust blog ranking using multi-criteria decision analysis AHP and TOPSIS. In IT Convergence and Security 2017 (pp. 68-76). Springer, Singapore. #
[26] Cano, A.E., Mazumdar, S. and Ciravegna, F., 2014. Social influence analysis in microblogging platforms–a topic-sensitive based approach. Semantic Web, 5(5), pp.357-372. #
[27] Devi, P.S. and Karthika, S., 2019. # CycloneGaja-rank based credibility analysis system in social media during the crisis. Procedia Computer Science, 165, pp.684-690. #
[28] Embar, V.R., Bhattacharya, I., Pandit, V. and Vaculin, R., 2015, August. Online topic-based social influence analysis for the wimbledon championships. In Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 1759-1768). #
[29] Gupta, S., Sachdeva, S., Dewan, P. and Kumaraguru, P., 2018, December. CbI: Improving Credibility of User-Generated Content on Facebook. In International Conference on Big Data Analytics (pp. 170-187). Springer, Cham. #
[30] Hamzehei, A., Wong, R.K., Koutra, D. and Chen, F., 2019. Collaborative topic regression for predicting topic-based social influence. Machine Learning, 108(10), pp.1831-1850 #
[31] Setiawan, E.B., Widyantoro, D.H. and Surendro, K., 2020. Measuring information credibility in social media using combination of user profile and message content dimensions. International Journal of Electrical & Computer Engineering (2088-8708), 10(4). #
[32] Son, J., Lee, J., Oh, O., Lee, H.K. and Woo, J., 2020. Using a Heuristic-Systematic Model to assess the Twitter user profile’s impact on disaster tweet credibility. International Journal of Information Management, 54, p.102176. #
[33] Zhao, L., Hua, T., Lu, C.T. and Chen, R., 2016. A topic-focused trust model for Twitter. Computer Communications, 76, pp.1-11. #
[34] Sicilia, R., Giudice, S.L., Pei, Y., Pechenizkiy, M. and Soda, P., 2018. Twitter rumour detection in the health domain. Expert Systems with Applications, 110, pp.33-40 #
[35] O'Brien, K., Simek, O. and Waugh, F., 2019, January. Collective classification for social media credibility estimation. In Proceedings of the 52nd Hawaii International Conference on System Sciences. #
[36] Zarrinkalam F, Kahani M, Bagheri E. Mining user interests over active topics on social networks. Information Processing & Management. 2018 Mar 1;54(2):339-57. #
[37] Viviani, M. and Pasi, G., 2017. Credibility in social media: opinions, news, and health information—a survey. Wiley interdisciplinary reviews: Data mining and knowledge discovery, 7(5), p.e1209. #
[38] Momen Bhuiyan, M., Horning, M., Lee, S.W. and Mitra, T., 2021. NudgeCred: Supporting News Credibility Assessment on Social Media Through Nudges. arXiv e-prints, pp.arXiv-2108. #
[39] Daud, N.N., Ab Hamid, S.H., Saadoon, M., Sahran, F. and Anuar, N.B., 2020. Applications of link prediction in social networks: A review. Journal of Network and Computer Applications, 166, p.102716