Data Collection and Algorithmic Influence on TikTok and Facebook, Assignments of Communication

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TABLE OF

• I. Introduction ____________________________________________________________ CONTENT
• II. Analysis _______________________________________________________________
  • 1. How TikTok & Facebook Collect and Use Da ta
  • 2. How Data Shapes Your Feed: The Power of Algorithms ______________________
  • 3. Short-Form Video: The Data-Driven Trend ________________________________
  • 4. Social Commerce: Data-Driven Shopping Trends
• III. Conclusion ____________________________________________________________
• References _______________________________________________________________

II. Analysis

1. How TikTok & Facebook Collect and Use Data

User data collection is fundamental to TikTok and Facebook’s strategies for content personalization and advertising optimization. These platforms rely on behavioral data, demographic information, and artificial intelligence (AI) to enhance user engagement and drive revenue. 2024 2025 80 60 40 20 0 Facebook Tiktok Platform Figure 1: User Numbers on Facebook and TikTok in Vietnam (2024–2025) A core aspect of data collection is tracking user behavior to refine content recommendations. TikTok records video watch time, likes, shares, comments, and scrolling behavior to optimize its For You Page algorithm, ensuring users receive content aligned with their preferences (Macarthy, 2021; Liu & Li, 2023). In contrast, Facebook adjusts its News Feed by prioritizing posts based on user interactions with content, fan pages, and groups (Thubron, 2018). The effectiveness of these techniques is reflected in the platforms' growing user bases. In Vietnam alone, TikTok and Facebook had 67.72 million and 72. million users in early 2024, respectively (DataReportal, 2024). By early 2025, Facebook’s user base in Vietnam had increased to 76.2 million, while TikTok had 40.9 million users aged 18 and above (DataReportal, 2025). This demonstrates how large-scale behavioral data collection drives user engagement and platform expansion. Beyond behavior, demographic data also plays a vital role. Facebook collects user profile information —such as age, gender, location, and device usage—alongside search histories to tailor content to audience segments (Meta, 2023). In contrast, TikTok emphasizes real-time interaction data, relying less on profiles and more on AI to interpret user preferences through video engagement (Chen, Lu & Yang, 2022). TikTok’s algorithm also analyzes visual, audio, and textual content to identify trends, while Facebook uses deep learning to optimize post visibility. Research shows TikTok can predict user interests within minutes, highlighting the platform's algorithmic precision (Geyser, 2023). Collected data primarily supports targeted advertising. Both platforms use user insights to deliver personalized ads that increase engagement and conversions (Statista, 2023). However, these practices raise ethical concerns. Facebook’s Cambridge Analytica scandal revealed misuse of data without user consent, while TikTok has been accused of promoting emotionally manipulative content to maximize watch time (Isaak & Hanna, 2018). From the Critical Data Studies perspective, these issues reflect the power imbalance between platforms and users, where people become both the producers and products of digital economies. Problems such as algorithmic opacity, limited user control, and data commodification challenge traditional ideas of privacy and autonomy. As such, there is a growing need for transparency and regulation in how digital platforms collect, use, and govern personal data. 72.70 76.2 67.72 40. Million

2. How Data Shapes Your Feed: The Power of Algorithms Algorithms built on user data play a pivotal role in shaping what content appears on users’ feeds across platforms. TikTok’s algorithm prioritizes videos based on user engagement metrics such as watch time, likes, shares, and comments. For instance, if a user repeatedly watches beauty-related videos, TikTok will recommend similar content (Zhou, 2024). Likewise, Facebook arranges its news feed based on user interactions with posts, fan pages, and groups. If someone frequently likes football-related content, Facebook will show more posts from sports pages (Lang, 2023). The platform’s algorithm utilizes artificial intelligence to rank content, ensuring relevance but potentially creating “filter bubbles” that limit exposure to diverse perspectives (Lauron, 2025; TikTok, 2020). This phenomenon demonstrates both the promise and risks of datafication , as content personalization can simultaneously enhance user experience and reinforce echo chambers. In addition to engagement-based signals, algorithms also rely heavily on users’ search and click behavior to shape content recommendations. TikTok instantly personalizes feeds based on search queries; for example, searching for "Da Lat travel" will result in more related videos appearing. Similarly, Facebook tracks click behavior to refine content suggestions and target ads. A user clicking on a sneaker ad is likely to see more similar ads in the future (Koç, 2023). Moreover, social connections and group participation influence content distribution. Facebook prioritizes posts from frequently engaged friends and groups, while TikTok recommends videos from creators within a user’s areas of interest (Sadhu, 2023; TikTok, 2020). In addition to social interactions, Facebook also utilizes user data, including location information, to refine content visibility and ad targeting. For instance, location tracking enables Facebook to display both organic posts and advertisements that are relevant to nearby businesses and local interests.

3. Short-Form Video: The Data-Driven Trend Figure 4: Viral Identity: The Jennie Effect on TikTok A vivid example of this is the #likejennie trend, where millions of users participated in dance covers inspired by BLACKPINK’s Jennie. The hashtag accumulated over 143,1K posts, showcasing TikTok’s potential to amplify content from even small accounts through personalized algorithmic boosts. TikTok’s system promotes content based purely on behavioral data, not follower count, helping trends go viral organically. Meanwhile, Facebook Reels uses user data to recommend content, such as gym-related videos for users who often engage with fitness topics. However, Facebook’s algorithm continues to prioritize social connections—posts from friends, followed pages, or groups—limiting its viral reach compared to TikTok. This contrast highlights a key difference: TikTok’s focus on engagement-based personalization enables dynamic content discovery, while Facebook’s socially oriented feed supports a more traditional, community-driven experience. 4. Social Commerce: Data-Driven Shopping Trends Figure 5: Data-Driven Shopping: TikTok Shop vs Facebook Marketplace in Vietnam

4. Social Commerce: Data-Driven Shopping Trends

Social commerce is booming in Vietnam as TikTok and Facebook use user data to personalize shopping. TikTok Shop leads with its algorithm-driven model, where livestreams and short videos boost visibility and drive impulsive purchases through trends and influencers. Vietnam now accounts for 17.5% of global TikTok Shop activity, behind only Indonesia and Thailand (Statista, 2025). Facebook Marketplace, meanwhile, recommends products based on demographics, location, and user activity in a more community-oriented, planned setting. While both platforms use machine learning, TikTok’s real-time behavioral targeting gives it an edge in surfacing emerging consumer interests dynamically. This data-driven distinction is reflected in commercial outcomes. According to Vietdata (2024), TikTok Shop recorded a revenue growth of approximately 180% in Vietnam between 2022 and 2023, reaching nearly VND 21 trillion. This impressive surge highlights TikTok’s ability to convert user engagement into purchasing behavior, particularly through livestream sales and trending short-form videos. Statista (2024) further reports that the fashion and accessories category alone generated around VND 8.1 trillion in Q1 2024, solidifying the platform’s dominance in high-demand product segments. These features empower even emerging sellers to gain traction by aligning their content with algorithm-driven recommendations. Such strategies depend on the platforms’ advanced data capabilities, turning micro-interactions into insights that influence real-time commerce dynamics. Meanwhile, Facebook Marketplace continues to serve as a foundational platform for small and medium-sized enterprises (SMEs) in Vietnam. SMEs account for 96.7% of all businesses in the country and are increasingly turning to digital platforms for marketing and customer engagement (Alpuerto, 2021). Facebook supports this sector by offering tools and ad policies tailored to help SMEs connect with relevant audiences both domestically and internationally. While Facebook’s marketplace model emphasizes community-driven discovery and static listings, it lacks the real-time interactivity and entertainment-shopping fusion that defines TikTok’s commerce ecosystem. This contrast highlights Vietnam’s shift toward spontaneous, data-personalized shopping behaviors enabled by video-centric social platforms

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