Abstract
Bias is inevitable and inherent in any form of communication. News often appear biased to citizens with different political orientations, and understood differently by news media scholars and the broader
public. In this paper we advocate the need for accurate methods for bias
identification in video news item, to enable rich analytics capabilities in
order to assist humanities media scholars and social political scientists.
We propose to analyze biases that are typical in video news (including
framing, gender and racial biases) by means of a human-in-the-loop approach that combines text and image analysis with human computation
techniques.
public. In this paper we advocate the need for accurate methods for bias
identification in video news item, to enable rich analytics capabilities in
order to assist humanities media scholars and social political scientists.
We propose to analyze biases that are typical in video news (including
framing, gender and racial biases) by means of a human-in-the-loop approach that combines text and image analysis with human computation
techniques.
| Original language | English |
|---|---|
| Title of host publication | Joint Proceedings SAD 2018 and CrowdBias 2018 |
| Place of Publication | Zurich |
| Pages | 32-40 |
| Number of pages | 9 |
| Volume | 2276 |
| Publication status | Published - 2018 |
Keywords
- Bias detection
- bias in news video files
- machine learning
- crowdsourcing
- human computation
- human in the loop
- digital humanities