InfluenceRank: A machine learning approach to measure influence of Twitter users

被引：0

作者：

Nargundkar, Ashish ^{[1
]}

Rao, Y. S. ^{[2
]}

机构：

[1] Sardar Patel Inst Technol, Dept Comp Engn, Bombay, Maharashtra, India

[2] Sardar Patel Inst Technol, Dept Elect & Telecommun Engn, Bombay, Maharashtra, India

来源：

2016 5TH INTERNATIONAL CONFERENCE ON RECENT TRENDS IN INFORMATION TECHNOLOGY (ICRTIT) | 2016年

关键词：

social media influence; Twitter; machine learning; PageRank; support vector regression; influence measurement;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

We devise a system for measuring influence of Twitter users, which we call InfluenceRank, based on certain features extracted from their Twitter profiles and tweets authored over the duration of two months. As in the real world, influence of a user on social media may be judged by the engagement they drive through the content they publish. For a tweet, engagement can be most obviously measured by the number of retweets (RTs), favourites and replies it gets. Our system comprises of a regression based machine learning approach with InfluenceRank as the predictor variable against the set of our proposed features. The regression model has shown reasonable accuracy despite being fit on limited data.

引用

页数：6

共 50 条

[21] Approach Based on Ontology and Machine Learning for Identifying Causes Affecting Personality Disorder Disease on Twitter
Ellouze, Mourad
Mechti, Seifeddine
Belguith, Lamia Hadrich
KNOWLEDGE SCIENCE, ENGINEERING AND MANAGEMENT, PT III, 2021, 12817 : 659 - 669
[22] Detecting Potentially Harmful and Protective Suicide-Related Content on Twitter: Machine Learning Approach
Metzler, Hannah
Baginski, Hubert
Niederkrotenthaler, Thomas
Garcia, David
JOURNAL OF MEDICAL INTERNET RESEARCH, 2022, 24 (08)
[23] A Machine Learning Approach for Disease Surveillance and Visualization using Twitter Data
Ashok, Ashwin
Guruprasad, M.
Prakash, C. O.
Shylaja, S. S.
2019 SECOND INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE IN DATA SCIENCE (ICCIDS 2019), 2019,
[24] Monitoring Misinformation on Twitter During Crisis Events: A Machine Learning Approach
Hunt, Kyle
Agarwal, Puneet
Zhuang, Jun
RISK ANALYSIS, 2022, 42 (08) : 1728 - 1748
[25] Machine Learning-Based Approach to Analyze Sentiments on Moroccan Higher Education Through Twitter
Lasri, Imane
El-Marzouki, Naoufal
Riadsolh, Anouar
Elbelkacemi, Mourad
PROCEEDINGS OF NINTH INTERNATIONAL CONGRESS ON INFORMATION AND COMMUNICATION TECHNOLOGY, ICICT 2024, VOL 4, 2024, 1014 : 505 - 517
[26] Detecting Spam in Twitter Microblogging Services: A Novel Machine Learning Approach based on Domain Popularity
Binsaeed K.
Stringhini G.
Youssef A.E.
Intl. J. Adv. Comput. Sci. Appl., 2020, 11 (11-22): : 11 - 22
[27] Understanding the Language of ISIS: An Empirical Approach to Detect Radical Content on Twitter Using Machine Learning
Ul Rehman, Zia
Abbas, Sagheer
Khan, Muhammad Adnan
Mustafa, Ghulam
Fayyaz, Hira
Hanif, Muhammad
Saeed, Muhammad Anwar
CMC-COMPUTERS MATERIALS & CONTINUA, 2021, 66 (02): : 1075 - 1090
[28] Tweet Topics and Sentiments Relating to COVID-19 Vaccination Among Australian Twitter Users: Machine Learning Analysis
Kwok, Stephen Wai Hang
Vadde, Sai Kumar
Wang, Guanjin
JOURNAL OF MEDICAL INTERNET RESEARCH, 2021, 23 (05)
[29] Detecting Spam in Twitter Microblogging Services: A Novel Machine Learning Approach based on Domain Popularity
Binsaeed, Khalid
Stringhini, Gianluca
Youssef, Ahmed E.
INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 2020, 11 (11) : 11 - 22
[30] Machine and Deep Learning Algorithms for Twitter Spam Detection
Alsaffar, Dalia
Alfahhad, Amjad
Alqhtani, Bashaier
Alamri, Lama
Alansari, Shahad
Alqahtani, Nada
Alboaneen, Dabiah A.
PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON ADVANCED INTELLIGENT SYSTEMS AND INFORMATICS 2019, 2020, 1058 : 483 - 491

← 1 2 3 4 5 →