Finding Context-Based Influencers on Twitter

被引:0
|
作者
Krishna R. [1 ]
Prashanth C.M. [2 ]
机构
[1] Department of Information Science and Engineering, Sri Krishna Institute of Technology, Karnataka, Bangalore
[2] Mangalore Institute of Technology and Engineering, Karnataka, Moodabidre
关键词
Influence score; Influential user; Sentiment analysis; Social media marketing;
D O I
10.1007/s42979-023-02470-0
中图分类号
学科分类号
摘要
With the vast number of people present on online social network platforms like Twitter, Instagram, Facebook, etc., many business people looked into this number as a business opportunity. Thus, they started converting this number to business with the help of influencer marketing. An influencer is a person who can change the opinion or purchase behavior of people who are following them. Marketing that relies on individuals with strong social media presence to promote brands is called influence marketing. Due to their authority in a specific field or industry, influencers influence their fans’ buying habits and beliefs through their persuasive powers. Thus, determining influencers in the network is currently a popular research topic. The influence score of a person has been calculated in two ways: one based on the structure of the network and the user’s connectivity in the network, and another based on the user’s activity in the network. Research has been going on to find the influence of the user on social networks based on the user’s behavior and engagement ratio. This work focuses on finding the influencer who positively influences the given context. This paper proposes a method to find the context-based positive influence. We also need to consider the user’s sentiment before finding the list of influential users. The result shows that even if we find the influential user if the influence isn’t positive, the influence marketing may have a negative effect. © 2024, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.
引用
收藏
相关论文
共 50 条
  • [41] Context-based fuzzy system for optimization
    Widjaja, M
    Sugianto, LF
    PROCEEDINGS OF THE 2002 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOL 1 & 2, 2002, : 104 - 109
  • [42] Context-based semantics for information integration
    Serafini, L
    Ghidini, C
    FORMAL ASPECTS OF CONTEXT, 2000, 20 : 175 - 192
  • [43] Context-based image similarity queries
    Bartolini, I
    ADAPTIVE MULTIMEDIA RETRIEVAL: USER, CONTEXT, AND FEEDBACK, 2006, 3877 : 222 - 235
  • [44] Context-based naming in information bases
    Theodorakis, M
    Constantopoulos, P
    INTERNATIONAL JOURNAL OF COOPERATIVE INFORMATION SYSTEMS, 1997, 6 (3-4) : 269 - 292
  • [45] A context-based vehicular communication protocol
    Chisalita, L
    Shahmehri, N
    2004 IEEE 15TH INTERNATIONAL SYMPOSIUM ON PERSONAL, INDOOR AND MOBILE RADIO COMMUNICATIONS, VOLS 1-4, PROCEEDINGS, 2004, : 2820 - 2824
  • [46] On the formation of context-based person impressions
    Huang, Lisa M.
    Sacchi, Dario L. M.
    Sherman, Jeffrey W.
    JOURNAL OF EXPERIMENTAL SOCIAL PSYCHOLOGY, 2017, 68 : 146 - 156
  • [47] A context-based infrastructure for smart environments
    Dey, AK
    Abowd, GD
    Salber, D
    MANAGING INTERACTIONS IN SMART ENVIRONMENTS, 2000, : 114 - 128
  • [48] Context-based segmentation of image sequences
    Goldberger, J
    Greenspan, H
    IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2006, 28 (03) : 463 - 468
  • [49] Global context-based value prediction
    Nakra, T
    Gupta, R
    Soffa, ML
    FIFTH INTERNATIONAL SYMPOSIUM ON HIGH-PERFORMANCE COMPUTER ARCHITECTURE, PROCEEDINGS, 1999, : 4 - 12
  • [50] Context-based prediction and neural variability
    Bruckmaier, Merit
    Zinchenko, Artyom
    Mueller, Hermann J.
    Geyer, Thomas
    PERCEPTION, 2021, 50 (1_SUPPL) : 17 - 17