Studying Behavioral Addiction by Combining Surveys and Digital Traces: A Case Study of TikTok

• URL: http://arxiv.org/abs/2501.15539v1
• Date: Sun, 26 Jan 2025 14:24:07 GMT
• Title: Studying Behavioral Addiction by Combining Surveys and Digital Traces: A Case Study of TikTok
• Authors: Cai Yang, Sepehr Mousavi, Abhisek Dash, Krishna P. Gummadi, Ingmar Weber,
• Abstract summary: We study if one can effectively diagnose behavioral addiction using digital data traces from social media platforms. We survey 1590 TikTok users and stratify them into three addiction groups. By analyzing users' data, we find that highly likely addicted users spend more time watching TikTok videos and keep coming back to TikTok throughout the day.
• Score: 8.709322238283734
• License:
• Abstract: Opaque algorithms disseminate and mediate the content that users consume on online social media platforms. This algorithmic mediation serves users with contents of their liking, on the other hand, it may cause several inadvertent risks to society at scale. While some of these risks, e.g., filter bubbles or dissemination of hateful content, are well studied in the community, behavioral addiction, designated by the Digital Services Act (DSA) as a potential systemic risk, has been understudied. In this work, we aim to study if one can effectively diagnose behavioral addiction using digital data traces from social media platforms. Focusing on the TikTok short-format video platform as a case study, we employ a novel mixed methodology of combining survey responses with data donations of behavioral traces. We survey 1590 TikTok users and stratify them into three addiction groups (i.e., less/moderately/highly likely addicted). Then, we obtain data donations from 107 surveyed participants. By analyzing users' data we find that, among others, highly likely addicted users spend more time watching TikTok videos and keep coming back to TikTok throughout the day, indicating a compulsion to use the platform. Finally, by using basic user engagement features, we train classifier models to identify highly likely addicted users with $F_1 \geq 0.55$. The performance of the classifier models suggests predicting addictive users solely based on their usage is rather difficult.

Related papers

• How Unique is Whose Web Browser? The role of demographics in browser fingerprinting among US users [50.699390248359265]
  Browser fingerprinting can be used to identify and track users across the Web, even without cookies. This technique and resulting privacy risks have been studied for over a decade. We provide a first-of-its-kind dataset to enable further research.
  arXiv  Detail & Related papers  (2024-10-09T14:51:58Z)
• Modeling offensive content detection for TikTok [0.0]
  This research undertakes the collection and analysis of TikTok data containing offensive content. It builds a series of machine learning and deep learning models for offensive content detection.
  arXiv  Detail & Related papers  (2024-08-29T18:47:41Z)
• TikTok Engagement Traces Over Time and Health Risky Behaviors: Combining Data Linkage and Computational Methods [13.061341132181097]
  This study investigates how individuals' liked TikTok videos on various health-risk topics are associated with their vaping and drinking behaviors. A computational analysis of 13,724 health-related videos liked by these respondents from 2020 to 2023 was conducted. Our findings indicate that users who initially liked drinking-related content on TikTok are inclined to favor more of such videos over time.
  arXiv  Detail & Related papers  (2024-06-23T02:58:30Z)
• Measuring Strategization in Recommendation: Users Adapt Their Behavior to Shape Future Content [66.71102704873185]
  We test for user strategization by conducting a lab experiment and survey. We find strong evidence of strategization across outcome metrics, including participants' dwell time and use of "likes" Our findings suggest that platforms cannot ignore the effect of their algorithms on user behavior.
  arXiv  Detail & Related papers  (2024-05-09T07:36:08Z)
• User Strategization and Trustworthy Algorithms [81.82279667028423]
  We show that user strategization can actually help platforms in the short term. We then show that it corrupts platforms' data and ultimately hurts their ability to make counterfactual decisions.
  arXiv  Detail & Related papers  (2023-12-29T16:09:42Z)
• Analyzing User Engagement with TikTok's Short Format Video Recommendations using Data Donations [31.764672446151412]
  We analyze user engagement on TikTok using data we collect via a data donation system. We find that the average daily usage time increases over the users' lifetime while the user attention remains stable at around 45%. We also find that users like more videos uploaded by people they follow than those recommended by people they do not follow.
  arXiv  Detail & Related papers  (2023-01-12T11:34:45Z)
• D-BIAS: A Causality-Based Human-in-the-Loop System for Tackling Algorithmic Bias [57.87117733071416]
  We propose D-BIAS, a visual interactive tool that embodies human-in-the-loop AI approach for auditing and mitigating social biases. A user can detect the presence of bias against a group by identifying unfair causal relationships in the causal network. For each interaction, say weakening/deleting a biased causal edge, the system uses a novel method to simulate a new (debiased) dataset.
  arXiv  Detail & Related papers  (2022-08-10T03:41:48Z)
• An Empirical Investigation of Personalization Factors on TikTok [77.34726150561087]
  Despite the importance of TikTok's algorithm to the platform's success and content distribution, little work has been done on the empirical analysis of the algorithm. Using a sock-puppet audit methodology with a custom algorithm developed by us, we tested and analysed the effect of the language and location used to access TikTok. We identify that the follow-feature has the strongest influence, followed by the like-feature and video view rate.
  arXiv  Detail & Related papers  (2022-01-28T17:40:00Z)
• Learning Language and Multimodal Privacy-Preserving Markers of Mood from Mobile Data [74.60507696087966]
  Mental health conditions remain underdiagnosed even in countries with common access to advanced medical care. One promising data source to help monitor human behavior is daily smartphone usage. We study behavioral markers of daily mood using a recent dataset of mobile behaviors from adolescent populations at high risk of suicidal behaviors.
  arXiv  Detail & Related papers  (2021-06-24T17:46:03Z)
• Learning User Embeddings from Temporal Social Media Data: A Survey [15.324014759254915]
  We survey representative work on learning a concise latent user representation (a.k.a. user embedding) that can capture the main characteristics of a social media user. The learned user embeddings can later be used to support different downstream user analysis tasks such as personality modeling, suicidal risk assessment and purchase decision prediction.
  arXiv  Detail & Related papers  (2021-05-17T16:22:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.