Pith. sign in

REVIEW 2 cited by

A Survey on Watching Social Issue Videos among YouTube and TikTok Users

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2310.19193 v1 pith:JFHUU3MJ submitted 2023-10-29 cs.HC cs.SI

classification cs.HCcs.SI
keywords userssivsinformationsocialtiktokyoutubevideosentertainment
verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments
read the original abstract

The openness and influence of video-sharing platforms (VSPs) such as YouTube and TikTok attracted creators to share videos on various social issues. Although social issue videos (SIVs) affect public opinions and breed misinformation, how VSP users obtain information and interact with SIVs is under-explored. This work surveyed 659 YouTube and 127 TikTok users to understand the motives for consuming SIVs on VSPs. We found that VSP users are primarily motivated by the information and entertainment gratifications to use the platform. VSP users use SIVs for information-seeking purposes and find YouTube and TikTok convenient to interact with SIVs. VSP users moderately watch SIVs for entertainment and inactively engage in social interactions. SIV consumption is associated with information and socialization gratifications of the platform. VSP users appreciate the diversity of information and opinions but would also do their own research and are concerned about the misinformation and echo chamber problems.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. FakeSV-VLM: Taming VLM for Detecting Fake Short-Video News via Progressive Mixture-Of-Experts Adapter

    cs.MM 2025-08 reject novelty 5.0 of 10

    FakeSV-VLM reaches 90.22% and 89.30% accuracy on FakeSV and FakeTT by adding a two-stage MoE adapter and contrastive alignment to InternVL2.5-8B.

  2. Consistency-aware Fake Videos Detection on Short Video Platforms

    cs.CV 2025-04 reject novelty 5.0 of 10

    CA-FVD detects fake news videos by using an MLLM to label cross-modal inconsistencies and fusing consistency and emotion scores, with reported accuracy gains on FakeSV and FakeTT.

Pith tools