# The Potential of Using Vision Videos for CrowdRE: Video Comments as a Source of Feedback

arXiv: https://arxiv.org/abs/2108.02076 · HTML: https://mlsanity.com/paper/2108.02076/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 53 references that are in the map.
- First posted on arXiv 4 Aug 2021.

## Builds on (most cited first, 9 of 9)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Sentiment Analysis on YouTube: A Brief Survey](https://mlsanity.com/paper/1511.09142/) (2015)
- [Video as a By-Product of Digital Prototyping: Capturing the Dynamic Aspect of Interaction](https://mlsanity.com/paper/1708.00278/) (2017)
- [Reframing Societal Discourse as Requirements Negotiation: Vision Statement](https://mlsanity.com/paper/1708.00279/) (2017)
- [Software Professionals are Not Directors: What Constitutes a Good Video?](https://mlsanity.com/paper/1808.04986/) (2018)
- [Software Professionals' Attitudes towards Video as a Medium in Requirements Engineering](https://mlsanity.com/paper/1809.00804/) (2018)
- [Refining Vision Videos](https://mlsanity.com/paper/1901.06677/) (2019)
- [Representing Software Project Vision by Means of Video: A Quality Model for Vision Videos](https://mlsanity.com/paper/1911.10400/) (2019)
- [Using Vision Videos in a Virtual Focus Group: Experiences and Recommendations](https://mlsanity.com/paper/2011.00965/) (2020)

## Abstract (arXiv)

Vision videos are established for soliciting feedback and stimulating discussions in requirements engineering (RE) practices, such as focus groups. Different researchers motivated the transfer of these benefits into crowd-based RE (CrowdRE) by using vision videos on social media platforms. So far, however, little research explored the potential of using vision videos for CrowdRE in detail. In this paper, we analyze and assess this potential, in particular, focusing on video comments as a source of feedback. In a case study, we analyzed 4505 comments on a vision video from YouTube. We found that the video solicited 2770 comments from 2660 viewers in four days. This is more than 50% of all comments the video received in four years. Even though only a certain fraction of these comments are relevant to RE, the relevant comments address typical intentions and topics of user feedback, such as feature request or problem report. Besides the typical user feedback categories, we found more than 300 comments that address the topic safety, which has not appeared in previous analyses of user feedback. In an automated analysis, we compared the performance of three machine learning algorithms on classifying the video comments. Despite certain differences, the algorithms classified the video comments well. Based on these findings, we conclude that the use of vision videos for CrowdRE has a large potential. Despite the preliminary nature of the case study, we are optimistic that vision videos can motivate stakeholders to actively participate in a crowd and solicit numerous of video comments as a valuable source of feedback.