Abstract
Corporate social media analysts break influencers into five tiers of increasing importance: Nano, Micro, Mid, Macro, and Mega. We perform a comprehensive study of TikTok influencers with two goals: (i) what factors distinguish influencers in each of these tiers from the adjacent tier(s)? (ii) of the features influencers can directly control ("actionable" features), which ones are most impactful to reach the next tier? We build and release a novel TikTok dataset featuring over 230K videos from 5000 influencers—1000 from each tier. The dataset includes video details such as likes, facial action units, emotions, and music information derived from Spotify. Access to the videos is facilitated through provided URLs and hydration code. To find the most important features that distinguish influencers in a tier from those in the next tier up, we thoroughly analyze traditional features (e.g., profile information) and text, audio, and video features using statistical methods and ablation testing. Our classifiers achieve F1-scores over 80%. The most impactful actionable features are traditional and video features, including enhancing video pleasure, quality, and emphasizing facial expressions. Finally, we collect and release a YouTube Shorts dataset to conduct a comparative analysis, aiming to identify similarities and differences between the two platforms.
Saurabh Kumar
Assistant Professor
My research interests include cybersecurity, Android security, malware analysis and ceyber forensics.