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
[41]   Fine-grained emoji sentiment analysis based on attributes of Twitter users [J].
Sun, Xiaoyu ;
Li, Huakang ;
Sun, Guozi ;
Zhu, Ming .
2020 IEEE INTERNATIONAL CONFERENCE ON SMART CLOUD (SMARTCLOUD 2020), 2020, :134-139
[42]   Demographical gender prediction of Twitter users using big data analytics: An application of decision marketing [J].
Roy S. ;
Patel B. ;
Bhattacharyya D. ;
Dhayal K. ;
Kim T.-H. ;
Mittal M. .
International Journal of Reasoning-based Intelligent Systems, 2021, 13 (02) :41-49
[43]   Can metadata be used to measure the anonymity of Twitter users? Results of a Confirmatory Factor Analysis [J].
Esteve, Zoraida ;
Moneva, Asier ;
Miro-Llinares, Fernando .
INTERNATIONAL E-JOURNAL OF CRIMINAL SCIENCES, 2019, (13)
[44]   Do Twitter users change their behavior after exposure to misinformation? An in-depth analysis [J].
Yichen Wang ;
Richard Han ;
Tamara Silbergleit Lehman ;
Qin Lv ;
Shivakant Mishra .
Social Network Analysis and Mining, 2022, 12
[45]   Public perception of COVID-19 vaccines through analysis of Twitter content and users [J].
Saleh, Sameh N. ;
McDonald, Samuel A. ;
Basit, Mujeeb A. ;
Kumar, Sanat ;
Arasaratnam, Reuben J. ;
Perl, Trish M. ;
Lehmann, Christoph U. ;
Medford, Richard J. .
VACCINE, 2023, 41 (33) :4844-4853
[46]   Do Twitter users change their behavior after exposure to misinformation? An in-depth analysis [J].
Wang, Yichen ;
Han, Richard ;
Lehman, Tamara Silbergleit ;
Lv, Qin ;
Mishra, Shivakant .
SOCIAL NETWORK ANALYSIS AND MINING, 2022, 12 (01)
[47]   We Know You Are Living in Bali: Location Prediction of Twitter Users Using BERT Language Model [J].
Simanjuntak, Lihardo Faisal ;
Mahendra, Rahmad ;
Yulianti, Evi .
BIG DATA AND COGNITIVE COMPUTING, 2022, 6 (03)
[48]   Who Are They and Where? Insights Into the Social and Spatial Dimensions of Imagined Audiences From a Mobile Diary Study of Twitter Users [J].
Stoltenberg, Daniela ;
Pfetsch, Barbara ;
Keinert, Alexa ;
Waldherr, Annie .
SOCIAL MEDIA + SOCIETY, 2022, 8 (03)
[49]   Who, Where, When, and What: A Nonparametric Bayesian Approach to Context-aware Recommendation and Search for Twitter Users [J].
Yuan, Quan Y ;
Cong, Gao ;
Zhao, Kaiqi ;
Ma, Zongyang ;
Sun, Aixin .
ACM TRANSACTIONS ON INFORMATION SYSTEMS, 2015, 33 (01) :2
[50]   Types of Racism and Twitter Users' Responses Amid the COVID-19 Outbreak: Content Analysis [J].
Lloret-Pineda, Amanda ;
He, Yuelu ;
Haro, Josep Maria ;
Cristobal-Narvaez, Paula .
JMIR FORMATIVE RESEARCH, 2022, 6 (05)