What sets Verified Users apart? Insights, Analysis and Prediction of Verified Users on Twitter

被引:1
作者
Paul, Indraneil [1 ]
Khattar, Abhinav [2 ]
Chopra, Shaan [2 ]
Kumaraguru, Ponnurangam [2 ]
Gupta, Manish [1 ,3 ]
机构
[1] IIIT Hyderabad, Hyderabad, India
[2] IIIT Delhi, Delhi, India
[3] Microsoft India, Hyderabad, India
来源
PROCEEDINGS OF THE 11TH ACM CONFERENCE ON WEB SCIENCE (WEBSCI'19) | 2019年
关键词
Twitter; Social Influence; Verified Users; INFORMATION;
D O I
10.1145/3292522.3326026
中图分类号
C [社会科学总论];
学科分类号
03 ; 0303 ;
摘要
Social network and publishing platforms, such as Twitter, support the concept of a secret proprietary verification process, for handles they deem worthy of platform-wide public interest. In line with significant prior work which suggests that possessing such a status symbolizes enhanced credibility in the eyes of the platform audience, a verified badge is clearly coveted among public figures and brands. What are less obvious are the inner workings of the verification process and what being verified represents. This lack of clarity, coupled with the flak that Twitter received by extending aforementioned status to political extremists in 2017, backed Twitter into publicly admitting that the process and what the status represented needed to be rethought. With this in mind, we seek to unravel the aspects of a user's profile which likely engender or preclude verification. The aim of the paper is two-fold: First, we test if discerning the verification status of a handle from profile metadata and content features is feasible. Second, we unravel the features which have the greatest bearing on a handle's verification status. We collected a dataset consisting of profile metadata of all 231,235 verified English-speaking users (as of July 2018), a control sample of 175,930 non-verified English-speaking users and all their 494 million tweets over a one year collection period. Our proposed models are able to reliably identify verification status (Area under curve AUC > 99%). We show that number of public list memberships, presence of neutral sentiment in tweets and an authoritative language style are the most pertinent predictors of verification status. To the best of our knowledge, this work represents the first attempt at discerning and classifying verification worthy users on Twitter.
引用
收藏
页码:215 / 224
页数:10
相关论文
共 50 条
[21]   Twitter as Knowledge Commons: What Twitter Users Think and Do About Managing Misinformation Issues [J].
Kim, Kyung-Sun ;
Joanna, Sin Sei-Ching .
Proceedings of the Association for Information Science and Technology, 2024, 61 (01) :971-973
[22]   A review of features for the discrimination of twitter users: application to the prediction of offline influence [J].
Cossu, Jean-Valere ;
Labatut, Vincent ;
Dugue, Nicolas .
SOCIAL NETWORK ANALYSIS AND MINING, 2016, 6 (01)
[23]   Understanding recession response by Twitter users: A text analysis approach [J].
Nathanael, Garcia Krisnando .
HELIYON, 2024, 10 (01)
[24]   Analysis of Tweet Form's effect on users' engagement on Twitter [J].
Han, Xu ;
Gu, Xingyu ;
Peng, Shuai .
COGENT BUSINESS & MANAGEMENT, 2019, 6 (01) :1-15
[25]   Confirmatory Analysis on Influencing Factors When Mention Users in Twitter [J].
Li, Yueyang ;
Ding, Zhaoyun ;
Zhang, Xin ;
Liu, Bo ;
Zhang, Weice .
WEB TECHNOLOGIES AND APPLICATIONS: APWEB 2016 WORKSHOPS, WDMA, GAP, AND SDMA, 2016, 9865 :112-121
[26]   Understanding verified users' posting behavior from the perspective of human dynamics: a case study of Sina micro-blog [J].
Yi, Ming ;
Lu, Yingying ;
Deng, Weihua ;
Kun, Lu ;
Zhang, Zhanhao .
ASLIB JOURNAL OF INFORMATION MANAGEMENT, 2021, 73 (02) :221-239
[27]   GLDM: Geo-location prediction of twitter users with deep learning methods [J].
Al-Jamaan, Rawabe ;
Yklef, Mourad ;
Alothaim, Abdulrahman .
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2023, 45 (02) :2723-2734
[28]   Methods and Annotated Data Sets Used to Predict the Gender and Age of Twitter Users: Scoping Review [J].
O'Connor, Karen ;
Golder, Su ;
Weissenbacher, Davy ;
Klein, Ari Z. ;
Magge, Arjun ;
Gonzalez-Hernandez, Graciela .
JOURNAL OF MEDICAL INTERNET RESEARCH, 2024, 26
[29]   Demographics Analysis of Twitter Users who Tweeted on Psychological Articles and Tweets Analysis [J].
Udayakumar, Sajeev ;
Senadeera, Damith Chamalke ;
Yamunarani, Selvaraj ;
Cheon, Na Jin .
INNS CONFERENCE ON BIG DATA AND DEEP LEARNING, 2018, 144 :96-104
[30]   An analysis of Twitter users opinions on vaccines using Machine Learning techniques [J].
Hallberg, Artur Galvao ;
Cortes, Eduardo Gabriel ;
Couto Barone, Dante Augusto .
2021 IEEE 33RD INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE (ICTAI 2021), 2021, :1311-1315