How do authoritarian regimes strengthen global support for nondemocratic political systems? Roughly half of the users of the social media platform TikTok report getting news from social media influencers. Against this backdrop, authoritarian regimes have increasingly outsourced content creation to these influencers. To gain understanding of the extent of this phenomenon and the persuasive capabilities of these influencers, we collect comprehensive data on pro-China influencers on TikTok. We show that pro-China influencers have more engagement than state media. We then create a realistic clone of the TikTok app, and conduct a randomized experiment in which over 8,500 Americans are recruited to use this app and view a random sample of actual TikTok content. We show that pro-China foreign influencers are strikingly effective at increasing favorability toward China, while traditional Chinese state media causes backlash. The findings highlight the importance of influencers in shaping global public opinion.
The short-form video-sharing service TikTok has become an important platform in the social media landscape, with much of its popularity owed to its algorithmically-driven "For You Page" (FYP). This feature serves as the "home screen" for the platform and provides a personalized feed of content for each user. Unlike other social media services-where new users start their journey by explicitly signaling whom they choose to friend or follow-the TikTok FYP algorithm instead begins making inferences based on implicit signals, such as how long they watch particular videos. As a result, users have less explicit control over what content they see, and concerns have been raised about the impact on users (e.g., the delivery of potentially harmful content). In this work, we investigate the extent to which users have control over the content they see on the FYP on TikTok. We first develop novel techniques to study the TikTok mobile app, introducing a new avenue for conducting controlled experiments that enable us to send both explicit and implicit signals on the app. We then use these techniques to study the FYP algorithm based on accounts we control. We find that the FYP algorithm is sensitiv
TikTok, the social media platform that is popular among children and adolescents, offers a more restrictive "Under 13 Experience" exclusively for young users in the US, also known as TikTok's "Kids Mode". While prior research has studied various aspects of TikTok's regular mode, including privacy and personalization, TikTok's Kids Mode remains understudied, and there is a lack of transparency regarding its content curation and its safety and privacy protections for children. In this paper, (i) we propose an auditing methodology to comprehensively investigate TikTok's Kids Mode and (ii) we apply it to characterize the platform's content curation and determine the prevalence of child-directed content, based on regulations in the Children's Online Privacy Protection Act (COPPA). We find that 83% of videos observed on the "For You" page in Kids Mode are actually not child-directed, and even inappropriate content was found. The platform also lacks critical features, namely parental controls and accessibility settings. Our findings have important design and regulatory implications, as children may be incentivized to use TikTok's regular mode instead of Kids Mode, where they are known to
This study examines the factors that influence the adoption of TikTok as a learning tool for physical education (PE)-related content among tertiary students in the Philippines. The study applies the Technology Acceptance Model (TAM) and Uses and Gratification Theory (UGT) to assess Information Seeking, Personal Identity, Social Interaction, Entertainment, Perceived Usefulness (PU), Perceived Ease of Use (PEOU), and Intention to Use (IU). A cross-sectional design and Structural Equation Modeling (SEM) were employed. The sample included 1,075 regular TikTok users with an average age of 19 years, the majority of whom were female. The analysis revealed that PU and PEOU were the strongest predictors of IU TikTok for PE related content. The results indicate that TikTok provides an engaging and accessible medium that supports active learning and participation in PE. The study offers empirical evidence from the Philippines and contributes to the academic discussion on the role of short-form video platforms in PE.
Social media platforms facilitate the dissemination of science and access to it. However, gender inequalities in the participation and visibility of communicators persist. This study examined the differences in reach and audience response between YouTube and TikTok from a gender perspective. To do so, the ten most influential science accounts on YouTube and TikTok were selected, with the sample divided equally between men and women, to conduct a comparative study. A total of 4293 videos on TikTok and 4825 on YouTube were analyzed, along with 277,528 comments, considering metrics of views and interaction. The results show that on YouTube, men received more likes and views, while on TikTok, audience response was more balanced. The participation of women on both platforms also had a differential impact, as the number of women engaging with content on YouTube negatively correlated with interaction levels, whereas on TikTok, their impact was slightly positive. In conclusion, TikTok emerges as a more inclusive space for scientific communication, though structural challenges remain on both platforms, encouraging further research into strategies that promote gender equity in online science
Intelligent algorithms increasingly shape the content we encounter and engage with online. TikTok's For You feed exemplifies extreme algorithm-driven curation, tailoring the stream of video content almost exclusively based on users' explicit and implicit interactions with the platform. Despite growing attention, the dynamics of content amplification on TikTok remain largely unquantified. How quickly, and to what extent, does TikTok's algorithm amplify content aligned with users' interests? To address these questions, we conduct a sock-puppet audit, deploying bots with different interests to engage with TikTok's "For You" feed. Our findings reveal that content aligned with the bots' interests undergoes strong amplification, with rapid reinforcement typically occurring within the first 200 videos watched. While amplification is consistently observed across all interests, its intensity varies by interest, indicating the emergence of topic-specific biases. Time series analyses and Markov models uncover distinct phases of recommendation dynamics, including persistent content reinforcement and a gradual decline in content diversity over time. Although TikTok's algorithm preserves some co
Whether genuine communities can form on algorithmically-driven short-form video platforms like TikTok remains an open question, given that user interactions are often brief, dispersed, and difficult to trace. Building on theories of tie strength and online community formation, we examine whether eating disorder (ED) discourse on TikTok exhibits behavioral and emotional signatures of strong ties, including more frequent, reciprocal, and affectively intense interactions. In this paper, we analyze 43,040 ED-related TikTok videos and over 560,000 comments, alongside a Non-ED comparison dataset. We find that at the user-pair level, greater interaction frequency is associated with increasingly positive emotional expression, a pattern that is amplified in ED-related conversations. This trend is also reflected linguistically, with pairs that interact more frequently exhibiting more of a positive tone. At the same time, how a relationship starts matters: pairs that begin with positive exchanges usually stay mostly positive as they continue interacting, while pairs that begin negatively may add some positive exchanges over time but rarely become mostly positive. To contextualize these dynami
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 t
TikTok has gradually become one of the most pervasive social media platforms in our daily lives. While much can be said about the merits of platforms such as TikTok, there is a different kind of attention paid towards the political affect of social media today compared to its impact on other aspects of modern networked reality. I explored how users on TikTok discussed the crisis in Palestine that worsened in 2023. Using network analysis, I situate keywords representing the conflict and categorize them thematically based on a coding schema derived from politically and ideologically differentiable stances. I conclude that activism and propaganda are contending amongst themselves in the thriving space afforded by TikTok today.
Social media platforms have transformed global communication and interaction, with TikTok emerging as a critical tool for education, connection, and social impact, including in contexts where infrastructural resources are limited. Amid growing political discussions about banning platforms like TikTok, such actions can create significant ripple effects, particularly impacting marginalized communities. We present a study on Nepal, where a TikTok ban was recently imposed and lifted. As a low-resource country in transition where digital communication is rapidly evolving, TikTok enables a space for community engagement and cultural expression. In this context, we conducted an online survey (N=108) to explore user values, experiences, and strategies for navigating online spaces post-ban. By examining these transitions, we aim to improve our understanding of how digital technologies, policy responses, and cultural dynamics interact globally and their implications for governance and societal norms. Our results indicate that users express skepticism toward platform bans but often passively accept them without active opposition. Findings suggest the importance of institutionalizing collectiv
Modern algorithmic recommendation systems seek to engage users through behavioral content-interest matching. While many platforms recommend content based on engagement metrics, others like TikTok deliver interest-based content, resulting in recommendations perceived to be hyper-personalized compared to other platforms. TikTok's robust recommendation engine has led some users to suspect that the algorithm knows users "better than they know themselves," but this is not always true. In this paper, we explore TikTok users' perceptions of recommended content on their For You Page (FYP), specifically calling attention to unwanted recommendations. Through qualitative interviews of 14 current and former TikTok users, we find themes of frustration with recommended content, attempts to rid themselves of unwanted content, and various degrees of success in eschewing such content. We discuss implications in the larger context of folk theorization and contribute concrete tactical and behavioral examples of algorithmic persistence.
We study how TikTok affects demand for music on paid streaming platforms. We use Universal Music Group's (UMG) global withdrawal of its catalog from TikTok as a quasi-natural experiment. Recent work using this setting reaches mixed conclusions about whether TikTok promotes or cannibalizes streaming demand. We show that these findings can be reconciled by making the estimand explicit: with heavy-tailed exposure and outcomes, common difference-in-differences (DiD) implementations in levels, logs, and Poisson answer different economic questions. In our data, the top 10% of songs account for 96% of TikTok creations and 76% of Spotify streams, which makes the distinction between the typical song and the economically consequential song central. We find that removing TikTok access lowers Spotify demand for UMG titles, with losses concentrated among viral songs and little economically meaningful change for the long tail. Because the viral head accounts for a disproportionate share of listening and revenue, these losses drive aggregate implications. A TikTok creator-side analysis shows that some activity reallocates toward non-UMG audio when UMG content is unavailable. This substitution is
TikTok is one of the largest and fastest-growing social media sites in the world. TikTok features, however, such as voice transcripts, are often missing and other important features, such as OCR or video descriptions, do not exist. We introduce the Generative AI Enriched TikTok (GET-Tok) data, a pipeline for collecting TikTok videos and enriched data by augmenting the TikTok Research API with generative AI models. As a case study, we collect videos about the attempted coup in Peru initiated by its former President, Pedro Castillo, and its accompanying protests. The data includes information on 43,697 videos published from November 20, 2022 to March 1, 2023 (102 days). Generative AI augments the collected data via transcripts of TikTok videos, text descriptions of what is shown in the videos, what text is displayed within the video, and the stances expressed in the video. Overall, this pipeline will contribute to a better understanding of online discussion in a multimodal setting with applications of Generative AI, especially outlining the utility of this pipeline in non-English-language social media. Our code used to produce the pipeline is in a public Github repository: https://gi
With over a billion active users, TikTok's video-sharing service is currently one of the largest social media websites. This rise in TikTok's popularity has made the website a central platform for music discovery. In this paper, we analyze how TikTok helps to revitalize older songs. To do so, we use both the popularity of songs shared on TikTok and how the platform allows songs to propagate to other places on the Web. We analyze data from TokBoard, a website measuring such popularity over time, and Google Trends, which captures songs' overall Web search interest. Our analysis initially focuses on whether TokBoard can cause (Granger Causality) popularity on Google Trends. Next, we examine whether TikTok and Google Trends share the same virality patterns (via a Bass Model). To our knowledge, we are one of the first works to study song re-popularization via TikTok.
We present TikTok StitchGraph: a collection of 36 graphs based on TikTok stitches. With its rapid growth and widespread popularity, TikTok presents a compelling platform for study, yet given its video-first nature the network structure of the conversations that it hosts remains largely unexplored. Leveraging its recently released APIs, in combination with web scraping, we construct graphs detailing stitch relations from both a video- and user-centric perspective. Specifically, we focus on user multi-digraphs, with vertices representing users and edges representing directed stitch relations. From the user graphs, we characterize common communication patterns of the stitch using frequent subgraph mining, finding a preference for stars and star-like structures, an aversion towards cyclic structures, and directional disposition favoring in- and out-stars over mixed-direction structures. These structures are augmented with sentiment labels in the form of edge attributes. We then use these subgraphs for graph-level embeddings together with Graph2Vec, we show no clear distinction between topologies for different hashtag topic categories. Lastly, we compare our StitchGraphs to Twitter repl
The recent proliferation of short form video social media sites such as TikTok has been effectively utilized for increased visibility, communication, and community connection amongst trans/nonbinary creators online. However, these same platforms have also been exploited by right-wing actors targeting trans/nonbinary people, enabling such anti-trans actors to efficiently spread hate speech and propaganda. Given these divergent groups, what are the differences in network structure between anti-trans and pro-trans communities on TikTok, and to what extent do they amplify the effects of anti-trans content? In this paper, we collect a sample of TikTok videos containing pro and anti-trans content, and develop a taxonomy of trans related sentiment to enable the classification of content on TikTok, and ultimately analyze the reply network structures of pro-trans and anti-trans communities. In order to accomplish this, we worked with hired expert data annotators from the trans/nonbinary community in order to generate a sample of highly accurately labeled data. From this subset, we utilized a novel classification pipeline leveraging Retrieval-Augmented Generation (RAG) with annotated example
TikTok, a widely-used social media app boasting over a billion monthly active users, requires effective app quality assurance for its intricate features. Feature testing is crucial in achieving this goal. However, the multi-user interactive features within the app, such as live streaming, voice calls, etc., pose significant challenges for developers, who must handle simultaneous device management and user interaction coordination. To address this, we introduce a novel multi-agent approach, powered by the Large Language Models (LLMs), to automate the testing of multi-user interactive app features. In detail, we build a virtual device farm that allocates the necessary number of devices for a given multi-user interactive task. For each device, we deploy an LLM-based agent that simulates a user, thereby mimicking user interactions to collaboratively automate the testing process. The evaluations on 24 multi-user interactive tasks within the TikTok app, showcase its capability to cover 75% of tasks with 85.9% action similarity and offer 87% time savings for developers. Additionally, we have also integrated our approach into the real-world TikTok testing platform, aiding in the detection
Like other social media, TikTok is embracing its use as a search engine, developing search products to steer users to produce searchable content and engage in content discovery. Their recently developed product search recommendations are preformulated search queries recommended to users on videos. However, TikTok provides limited transparency about how search recommendations are generated and moderated, despite requirements under regulatory frameworks like the European Union's Digital Services Act. By suggesting that the platform simply aggregates comments and common searches linked to videos, it sidesteps responsibility and issues that arise from contextually problematic recommendations, reigniting long-standing concerns about platform liability and moderation. This position paper addresses the novelty of search recommendations on TikTok by highlighting the challenges that this feature poses for platform governance and offering a computational research agenda, drawing on preliminary qualitative analysis. It sets out the need for transparency in platform documentation, data access and research to study search recommendations.
The (re)creation and distribution of cultural products such as music are increasingly shaped by digital platforms. This study explores how TikTok and Spotify, situated in different governance and user contexts, could influence digital music production and reception within each platform and between each other. Focusing on daily hit song charts as the embodiment of platformization, we collected and analyzed a two-year longitudinal dataset on TikTok and Spotify. We tested the relationships between elements of platformization and hit song popularity within each platform, and examined cross-platform influence flow. Results reveal significant differences in major label, genre, and content features among hit songs on TikTok and Spotify, which can be explained by their distinct platformization practices. We also found some evidence that hit song popularity on Spotify might precede that on TikTok. This study illustrates both the platform-specific mechanisms of TikTok and Spotify and their interconnectedness in the cultural production ecosystem.
TikTok is now a massive platform, and has a deep impact on global events. Despite preliminary studies, issues remain in determining fundamental characteristics of the platform. We develop a method to extract a representative sample of >99% of posts from a given time range on TikTok, and use it to collect all posts from a full hour on the platform, alongside all posts from a single minute from each hour of a day. Through this, we obtain post metadata, video media, and comments from a close-to-complete slice of TikTok, and report the critical statistics of the platform. Notably, we estimate a total of 269 million posts produced on the day we looked at, that 18% of videos on the platform feature children, and that at least 0.5% of posts contain artificial intelligence-generated content.