Content moderation assistance through image caption generation
The rapid growth in digital media creation has led to an increased challenge in content moderation. Manual and automated moderation are susceptible to risks associated with a slower response time and false positives arising from unpredictable user inputs respectively. Image caption generation has be...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | Intelligent Systems with Applications |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667305325000158 |
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| _version_ | 1850078834707136512 |
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| author | Liam Kearns |
| author_facet | Liam Kearns |
| author_sort | Liam Kearns |
| collection | DOAJ |
| description | The rapid growth in digital media creation has led to an increased challenge in content moderation. Manual and automated moderation are susceptible to risks associated with a slower response time and false positives arising from unpredictable user inputs respectively. Image caption generation has been suggested as a viable content moderation tool, but there is a lack of real world deployment in this context. In this work, a collaborative approach is taken, where a machine learning model is used to assist human moderators in the approval and rejection of media within a scavenger hunt game. The proposed model is trained on the Flickr30k and MS Coco datasets to generate captions for images. The results demonstrate a 13% reduction in review times, indicating that human–machine collaboration contributes to mitigating the risk of unsustainable review backlog growth. Furthermore, fine-tuning the model led to a 28% reduction in review times when compared to the untuned model. Notably, this paper contributes to knowledge by demonstrating caption generation as a viable content moderation tool in addition to its sensitivity to accurate captions, whereby false positives risk a deterioration in moderator response time. |
| format | Article |
| id | doaj-art-fa76e18ad9ed4816b9ab2e708c188877 |
| institution | DOAJ |
| issn | 2667-3053 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Intelligent Systems with Applications |
| spelling | doaj-art-fa76e18ad9ed4816b9ab2e708c1888772025-08-20T02:45:27ZengElsevierIntelligent Systems with Applications2667-30532025-03-012520048910.1016/j.iswa.2025.200489Content moderation assistance through image caption generationLiam Kearns0AuraQ, 33 Graham Rd, Malvern, WR14 2HU, Worcestershire, UKThe rapid growth in digital media creation has led to an increased challenge in content moderation. Manual and automated moderation are susceptible to risks associated with a slower response time and false positives arising from unpredictable user inputs respectively. Image caption generation has been suggested as a viable content moderation tool, but there is a lack of real world deployment in this context. In this work, a collaborative approach is taken, where a machine learning model is used to assist human moderators in the approval and rejection of media within a scavenger hunt game. The proposed model is trained on the Flickr30k and MS Coco datasets to generate captions for images. The results demonstrate a 13% reduction in review times, indicating that human–machine collaboration contributes to mitigating the risk of unsustainable review backlog growth. Furthermore, fine-tuning the model led to a 28% reduction in review times when compared to the untuned model. Notably, this paper contributes to knowledge by demonstrating caption generation as a viable content moderation tool in addition to its sensitivity to accurate captions, whereby false positives risk a deterioration in moderator response time.http://www.sciencedirect.com/science/article/pii/S2667305325000158Content moderationCaption generationComputer visionMachine learning |
| spellingShingle | Liam Kearns Content moderation assistance through image caption generation Intelligent Systems with Applications Content moderation Caption generation Computer vision Machine learning |
| title | Content moderation assistance through image caption generation |
| title_full | Content moderation assistance through image caption generation |
| title_fullStr | Content moderation assistance through image caption generation |
| title_full_unstemmed | Content moderation assistance through image caption generation |
| title_short | Content moderation assistance through image caption generation |
| title_sort | content moderation assistance through image caption generation |
| topic | Content moderation Caption generation Computer vision Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S2667305325000158 |
| work_keys_str_mv | AT liamkearns contentmoderationassistancethroughimagecaptiongeneration |