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Querying and Tracking Influencers in Social Streams

Authors:

Karthik Subbian,

Charu C. Aggarwal,

Jaideep SrivastavaAuthors Info & Claims

WSDM '16: Proceedings of the Ninth ACM International Conference on Web Search and Data Mining

Pages 493 - 502

https://doi.org/10.1145/2835776.2835788

Published: 08 February 2016 Publication History

Abstract

Influence analysis is an important problem in social network analysis due to its impact on viral marketing and targeted advertisements. Most of the existing influence analysis methods determine the influencers in a static network with an influence propagation model based on pre-defined edge propagation probabilities. However, none of these models can be queried to find influencers in both context and time-sensitive fashion from a streaming social data. In this paper, we propose an approach to maintain real-time influence scores of users in a social stream using a topic and time-sensitive approach, while the network and topic is constantly evolving over time. We show that our approach is efficient in terms of online maintenance and effective in terms various types of real-time context- and time-sensitive queries. We evaluate our results on both social and collaborative network data sets.

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Cited By

Zhao JWang PZhang WZhang ZLiu MTao JLui J(2024)Tracking Influencers in Decaying Social Activity Streams With Theoretical GuaranteesIEEE/ACM Transactions on Networking10.1109/TNET.2023.332302832:2(1461-1476)Online publication date: Apr-2024
https://doi.org/10.1109/TNET.2023.3323028
Porter IGalam BRamon-Gonen R(2023)Emotion detection and its influence on popularity in a social network-based on the American TV series FriendsSocial Network Analysis and Mining10.1007/s13278-023-01133-513:1Online publication date: 25-Sep-2023
https://doi.org/10.1007/s13278-023-01133-5
Hai-Jew S(2022)Self-Presenting Virtually for Remote Social InfluencePractical Peer-to-Peer Teaching and Learning on the Social Web10.4018/978-1-7998-6496-7.ch013(407-461)Online publication date: 2022
https://doi.org/10.4018/978-1-7998-6496-7.ch013
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Index Terms

Querying and Tracking Influencers in Social Streams

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cover image ACM Conferences

WSDM '16: Proceedings of the Ninth ACM International Conference on Web Search and Data Mining

February 2016

746 pages

ISBN:9781450337168

DOI:10.1145/2835776

General Chairs:
Paul N. Bennett
Microsoft Research
,
Vanja Josifovski
Pinterest
,
Program Chairs:
Jennifer Neville
Purdue University
,
Filip Radlinski
Microsoft

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 08 February 2016

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WSDM 2016: Ninth ACM International Conference on Web Search and Data Mining

February 22 - 25, 2016

California, San Francisco, USA

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WSDM '16 Paper Acceptance Rate 67 of 368 submissions, 18%;

Overall Acceptance Rate 498 of 2,863 submissions, 17%

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Cited By

Zhao JWang PZhang WZhang ZLiu MTao JLui J(2024)Tracking Influencers in Decaying Social Activity Streams With Theoretical GuaranteesIEEE/ACM Transactions on Networking10.1109/TNET.2023.332302832:2(1461-1476)Online publication date: Apr-2024
https://doi.org/10.1109/TNET.2023.3323028
Porter IGalam BRamon-Gonen R(2023)Emotion detection and its influence on popularity in a social network-based on the American TV series FriendsSocial Network Analysis and Mining10.1007/s13278-023-01133-513:1Online publication date: 25-Sep-2023
https://doi.org/10.1007/s13278-023-01133-5
Hai-Jew S(2022)Self-Presenting Virtually for Remote Social InfluencePractical Peer-to-Peer Teaching and Learning on the Social Web10.4018/978-1-7998-6496-7.ch013(407-461)Online publication date: 2022
https://doi.org/10.4018/978-1-7998-6496-7.ch013
Panchendrarajan RSaxena A(2022)Topic-based influential user detection: a surveyApplied Intelligence10.1007/s10489-022-03831-7Online publication date: 5-Jul-2022
https://doi.org/10.1007/s10489-022-03831-7
Loria ENacke LMarconi A(2021)On Social Contagion in GamificationProceedings of the ACM on Human-Computer Interaction10.1145/34746705:CHI PLAY(1-20)Online publication date: 6-Oct-2021
https://dl.acm.org/doi/10.1145/3474670
Weber PLudwig TBrodesser SGrönewald LKitamura YQuigley AIsbister KIgarashi TBjørn PDrucker S(2021)“It's a Kind of Art!”: Understanding Food Influencers as Influential Content CreatorsProceedings of the 2021 CHI Conference on Human Factors in Computing Systems10.1145/3411764.3445607(1-14)Online publication date: 6-May-2021
https://dl.acm.org/doi/10.1145/3411764.3445607
Almutairy MAlaskar HAlhumaid LAlkhalifah R(2021)Action-Aware Restricted Stream Influence Maximization Model to Identify Social InfluencersSocial Computing and Social Media: Applications in Marketing, Learning, and Health10.1007/978-3-030-77685-5_2(15-28)Online publication date: 3-Jul-2021
https://doi.org/10.1007/978-3-030-77685-5_2
Yang YPei J(2020)Influence Analysis in Evolving Networks: A SurveyIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2019.2934447(1-1)Online publication date: 2020
https://doi.org/10.1109/TKDE.2019.2934447
Huh JKim HRath BLu XSrivastava J(2020)You reap where you sow: a trust-based approach to initial seeding for viral advertisingInternational Journal of Advertising10.1080/02650487.2020.171882339:7(963-989)Online publication date: 31-Jan-2020
https://doi.org/10.1080/02650487.2020.1718823
Yukselen MMutlu AKaragoz PAkerkar RJung J(2019)Infuencee Oriented Topic PredictionProceedings of the 9th International Conference on Web Intelligence, Mining and Semantics10.1145/3326467.3326488(1-9)Online publication date: 26-Jun-2019
https://dl.acm.org/doi/10.1145/3326467.3326488
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