From Generality to Specificity: On Matter of Scale in Social Media Topic Communities

被引:1
|
作者
Vaganov, Danila [1 ]
Bardina, Mariia [1 ]
Guleva, Valentina [1 ]
机构
[1] ITMO Univ, 49 Kronverksky Pr, St Petersburg 197101, Russia
来源
基金
俄罗斯科学基金会;
关键词
Topic communities; On-line social media; Similarity group network; Personal involvement; General interest; Specific interest; Scaling phenomena; GENDER; SIMILARITY; MOTIVATION; INTERESTS; SCHOOLS; USERS;
D O I
10.1007/978-3-030-50423-6_23
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Research question stated in current paper concerns measuring significance of interest topic to a person on the base of digital footprints, observed in on-line social media. Interests are represented by online social groups in VK social network, which were marked by topics. Topic significance to a person is supposed to be related to the fraction of representative groups in user's subscription list. We imply that for each topic, depending on its popularity, relation to geographical region, and social acceptability, there is a value of group size which is significant. In addition, we suppose, that professional clusters of groups demonstrate relatively higher inner density and unify common groups. Therefore, following groups from more specific clusters indicate higher personal involvement to a topic - in this way, representative topical groups are marked. We build social group similarity graph, which is based on the number of common followers, extract subgraphs related to a single topic, and analyse bins of groups, build with increase of group sizes. Results show topics of general interests have higher density at larger groups in contrast to specific interests, which is in correspondence with initial hypothesis.
引用
收藏
页码:305 / 318
页数:14
相关论文
共 50 条
  • [1] GENERALITY AND TOPIC SPECIFICITY OF COGNITIVE STYLES
    PETERSON, C
    SCOTT, WA
    [J]. JOURNAL OF RESEARCH IN PERSONALITY, 1975, 9 (04) : 366 - 374
  • [2] Learning Topic Map from Large Scale Social Media Data
    Yang, Hui-Kuo
    [J]. WWW'20: COMPANION PROCEEDINGS OF THE WEB CONFERENCE 2020, 2020, : 279 - 283
  • [3] The Effect of Additive Regularization for Topic Modeling of Social Media Communities
    Fedorov, Andrey M.
    Datyev, Igor O.
    [J]. ARTIFICIAL INTELLIGENCE TRENDS IN SYSTEMS, VOL 2, 2022, 502 : 557 - 567
  • [4] Topic-aware joint analysis of overlapping communities and roles in social media
    Costa, Gianni
    Ortale, Riccardo
    [J]. INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS, 2020, 9 (04) : 415 - 429
  • [5] Topic-aware joint analysis of overlapping communities and roles in social media
    Gianni Costa
    Riccardo Ortale
    [J]. International Journal of Data Science and Analytics, 2020, 9 : 415 - 429
  • [6] Topic Sketch: Real Time Bursty Topic Detection From Social Media
    Keshav, B.
    Rajeshwari, J.
    [J]. 2017 INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTING AND CONTROL SYSTEMS (ICICCS), 2017, : 904 - 908
  • [7] Constructing Topic Hierarchies from Social Media Data
    Zhang, Yuhao
    Mao, Wenji
    Zeng, Daniel
    [J]. 2015 IEEE INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOP (ICDMW), 2015, : 1015 - 1018
  • [8] Topic Extraction in Social Media
    Rafea, Ahmed
    Mostafa, Nada A.
    [J]. PROCEEDINGS OF THE 2013 INTERNATIONAL CONFERENCE ON COLLABORATION TECHNOLOGIES AND SYSTEMS (CTS), 2013, : 94 - 98
  • [9] Browse by Chunks: Topic Mining and Organizing on Web-Scale Social Media
    Sang, Jitao
    Xu, Changsheng
    [J]. ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS, 2011, 7 (01)
  • [10] The Generality of Theory and the Specificity of Social Behavior: Contrasting Experimental and Hermeneutic Social Science
    Gantt, Edwin E.
    Lindstrom, Jeffrey P.
    Williams, Richard N.
    [J]. JOURNAL FOR THE THEORY OF SOCIAL BEHAVIOUR, 2017, 47 (02) : 130 - 153