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| An Empirical Study on Mobile Social Media Fatigue User Portraits in the New Media Environment：A Causality Perspective Based on SSO Theory |
| Zhang Yanfeng1, Peng Lihui1, Liu Jincheng2, Hong Chuang2 |
1.School of Public Management, Xiangtan University, Xiangtan 411105
2.School of Management, Jilin University, Changchun 130022 |
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Abstract This paper mainly explores the causal and outcome-based factors contributing to mobile social media fatigue by constructing a mobile social media burnout theory model and analyzing user portraits, providing guidance for companies to understand the development of the trend of mobile social media fatigue. This research was conducted by mining the psychological and behavioral characteristics of different types of social media fatigue among users, extracting the tags of fatigued users in mobile social media based on grounded theory and SSO theory, and taking farmers, students, and teachers as the survey subjects. Through the K-medoids clustering method, this paper makes an empirical analysis of four groups of user portraits with significant differences. According to the characteristics of the user's portrait tag, the types of fatigue user portraits in mobile social media can be divided into four categories, namely the diving neglected type, patient used type, platform transferred type, and the behavior substituted type. Next, this paper specifically analyzes the key characteristics of each type of user portrait to provide a more comprehensive explanation of the tag types of user portraits for mobile social media fatigue.
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Received: 25 October 2018
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1 WangZ B, CuiX H, GaoL, et al. A hybrid model of sentimental entity recognition on mobile social media[J]. EURASIP Journal on Wireless Communications and Networking, 2016, 2016: Article No. 253.
2 MasurP K, ReineckeL, ZiegeleM, et al. The interplay of intrinsic need satisfaction and Facebook specific motives in explaining addictive behavior on Facebook[J]. Computers in Human Behavior, 2014, 39: 376-386.
3 BrightL F, KleiserS B, GrauS L. Too much Facebook? An exploratory examination of social media fatigue[J]. Computers in Human Behavior, 2015, 44: 148-155.
4 PostGuest. Facebook is facing user fatigue[EB/OL]. [2018-07-22]. http://www.hypebot.com/hypebot/2017/02/facebook-facing-user-fatigue.html.
5 OghumaA P, Libaque-SaenzC F, WongS F, et al. An expectation-confirmation model of continuance intention to use mobile instant messaging[J]. Telematics and Informatics, 2016, 33(1): 34-47.
6 SunY Q, LiuD N, ChenS J, et al. Understanding users’ switching behavior of mobile instant messaging applications: An empirical study from the perspective of push-pull-mooring framework[J]. Computers in Human Behavior, 2017, 75: 727-738.
7 CramerE M, SongH, DrentA M. Social comparison on Facebook: Motivation, affective consequences, self-esteem, and Facebook fatigue[J]. Computers in Human Behavior, 2016, 64: 739-746.
8 TromholtM. The Facebook experiment: Quitting Facebook leads to higher levels of well-being[J]. Cyberpsychology, Behavior, and Social Networking, 2016, 19(11): 661-666.
9 LeeC C, ChouS T H, HuangY R. A study on personality traits and social media fatigue-example of facebook users[J]. Lecture Notes on Information Theory, 2014, 2(3): 249-253.
10 LinK M. Predicting Asian undergraduates’ intention to continue using social network services from negative perspectives[J]. Behaviour & Information Technology, 2015, 34(9): 882-892.
11 LuqmanA, CaoX F, AliA, et al. Empirical investigation of Facebook discontinues usage intentions based on SOR paradigm[J]. Computers in Human Behavior, 2017, 70: 544-555.
12 ZhangS W, ZhaoL, LuY B, et al. Do you get tired of socializing? An empirical explanation of discontinuous usage behaviour in social network services[J]. Information & Management, 2016, 53(7): 904-914.
13 ChenW H, LeeK H. Sharing, liking, commenting, and distressed? The pathway between Facebook interaction and psychological distress[J]. Cyberpsychology, Behavior, and Social Networking, 2013, 16(10): 728-734.
14 YaoJ, CaoX F. The balancing mechanism of social networking overuse and rational usage[J]. Computers in Human Behavior, 2017, 75: 415-422.
15 IglesiasJ A, AngelovP, LedezmaA, et al. Creating evolving user behavior profiles automatically[J]. IEEE Transactions on Knowledge and Data Engineering, 2012, 24(5): 854-867.
16 NasraouiO, SolimanM, SakaE, et al. A web usage mining framework for mining evolving user profiles in dynamic web sites[J]. IEEE Transactions on Knowledge and Data Engineering, 2008, 20(2): 202-215.
17 LiuQ N, NiuK, HeZ Q, et al. Microblog user interest modeling based on feature propagation[C]// Proceedings of the Sixth International Symposium on Computational Intelligence Design. New York: IEEE, 2013: 383-386.
18 AbelF, GaoQ, HoubenG J, et al. Analyzing user modeling on twitter for personalized news recommendations[C]// Proceedings of the International Conference on User Modeling, Adaptation, and Personalization. Heidelberg: Springer, 2011: 1-12.
19 KimJ Y, Collins-ThompsonK, BennettP N, et al. Characterizing web content, interestsuser, and search behavior by reading level and topic[C]// Proceedings of the Fifth ACM International Conference on Web Search and Data Mining. New York: ACM Press, 2012: 213-222.
20 王洋, 丁志刚, 郑树泉, 等. 一种用户画像系统的设计与实现[J]. 计算机应用与软件, 2018, 35(3): 8-14.
21 杨帆. 画像分析为基础的图书馆大数据实践——以国家图书馆大数据项目为例[J]. 图书馆论坛, 2019(2): 58-64.
22 熊伟, 杭波, 李兵, 等. 一种集成用户画像与内容的服务重定向方法[J]. 小型微型计算机系统, 2017, 38(12): 2762-2765.
23 马超. 基于主题模型的社交网络用户画像分析方法[D]. 合肥: 中国科学技术大学, 2017.
24 单晓红, 张晓月, 刘晓燕. 基于在线评论的用户画像研究——以携程酒店为例[J]. 情报理论与实践, 2018, 41(4): 99-104, 149.
25 韩梅花, 赵景秀. 基于“用户画像”的阅读疗法模式研究——以抑郁症为例[J]. 大学图书馆学报, 2017, 35(6): 105-110.
26 AyyagariR, GroverV, PurvisR. Technostress: Technological antecedents and implications[J]. MIS Quarterly, 2011, 35(4): 831-858.
27 GlaserB G, StraussA L, StrutzelE. The discovery of grounded theory: Strategies of qualitative research[J]. Nursing Research, 1968, 17(4): 364.
28 陈向明. 扎根理论的思路和方法[J]. 教育研究与实验, 1999(4): 58-63, 73.
29 刘鲁川, 李旭, 张冰倩. 基于扎根理论的社交媒体用户倦怠与消极使用研究[J]. 情报理论与实践, 2017, 40(12): 100-106, 51.
30 HsiaoK L. Compulsive mobile application usage and technostress: The role of personality traits[J]. Online Information Review, 2017, 41(2): 272-295.
31 KhatamiA, MirghasemiS, KhosraviA, et al. A new PSO-based approach to fire flame detection using K-Medoids clustering[J]. Expert Systems with Applications, 2017, 68: 69-80.
32 RaiA, UpadhyayS H. Bearing performance degradation assessment based on a combination of empirical mode decomposition and k-medoids clustering[J]. Mechanical Systems and Signal Processing, 2017, 93: 16-29.
33 PekerM. A decision support system to improve medical diagnosis using a combination of k-medoids clustering based attribute weighting and SVM[J]. Journal of Medical Systems, 2016, 40(5): 116.
34 ChenY M, LiuX P, LiX, et al. Delineating urban functional areas with building-level social media data：A dynamic time warping (DTW) distance based k-medoids method[J]. Landscape and Urban Planning, 2017, 160: 48-60. |
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