paper-with-me

Papers

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

2019-03-12 · Indraneil Paul, Abhinav Khattar, Shaan Chopra, Ponnurangam Kumaraguru, Manish Gupta

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.

📄 PDF Abstract BibTeX arXiv:1903.04879

Code (0)

등록된 구현이 없습니다.

Similar Papers 제목 키워드 기반

XAIR: A Framework of Explainable AI in Augmented Reality

2023-03-28 · Xuhai Xu, Mengjie Yu, Tanya R. Jonker, Kashyap Todi 외

Explainable AI (XAI) has established itself as an important component of AI-driven interactive systems. With Augmented Reality (AR) becoming more integrated in daily lives, the role of XAI also becomes essential in AR be…

Explainable Artificial Intelligence (XAI)

What Do Users Care About? Detecting Actionable Insights from User Feedback

2022-07-01 · NAACL (ACL) 2022 7 · Kasturi Bhattacharjee, Rashmi Gangadharaiah, Kathleen McKeown, Dan Roth

Users often leave feedback on a myriad of aspects of a product which, if leveraged successfully, can help yield useful insights that can lead to further improvements down the line. Detecting actionable insights can be ch…

Use of What-if Scenarios to Help Explain Artificial Intelligence Models for Neonatal Health

2024-10-12 · Abdullah Mamun, Lawrence D. Devoe, Mark I. Evans, David W. Britt 외

Early detection of intrapartum risk enables interventions to potentially prevent or mitigate adverse labor outcomes such as cerebral palsy. Currently, there is no accurate automated system to predict such events to assis…

counterfactualData Augmentation

Rent3D: Floor-Plan Priors for Monocular Layout Estimation

2015-06-01 · CVPR 2015 6 · Chenxi Liu, Alexander G. Schwing, Kaustav Kundu, Raquel Urtasun 외

The goal of this paper is to enable a 3D "virtual-tour" of an apartment given a small set of monocular images of different rooms, as well as a 2D floor plan. We frame the problem as inference in a Markov Random Field whi…

Integrating Floor Plans into Hedonic Models for Rent Price Appraisal

2021-02-16 · Kirill Solovev, Nicolas Pröllochs

Online real estate platforms have become significant marketplaces facilitating users' search for an apartment or a house. Yet it remains challenging to accurately appraise a property's value. Prior works have primarily s…