F2IND-IT! -- Multimodal Fuzzy Fake Indian News Detection using Images and Text
The paper presents a new framework for detecting fake news in Indian media by integrating visual and textual analysis. It employs advanced technologies such as ResNet-50 for image feature extraction and DistilBERT for text analysis, combined with a fuzzy inference system for reliability scoring. Experimental results indicate that this multimodal approach outperforms previous methods in accuracy and other performance metrics.
- ▪The framework addresses the challenges of misinformation in diverse media landscapes like India.
- ▪It combines visual features from news images and textual semantic embeddings for enhanced detection.
- ▪The model was evaluated on the IFND dataset and showed superior performance in accuracy, precision, recall, and F1-scores.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.17115 |
| Publication time | Tue, 19 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-19T04:04:57.272Z |
| Last seen | 2026-05-19T04:04:57.272Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | ktRrhCFFI2Ng |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Computer Science > Artificial Intelligence arXiv:2605.17115 (cs) [Submitted on 16 May 2026] Title:F2IND-IT! -- Multimodal Fuzzy Fake Indian News Detection using Images and Text Authors:Kushal Trivedi, Murtuza Shaikh, Khushi Singh, Jeevaraj S. View a PDF of the paper titled F2IND-IT! -- Multimodal Fuzzy Fake Indian News Detection using Images and Text, by Kushal Trivedi and 3 other authors View PDF HTML (experimental) Abstract:Biased manipulation of facts across regional and national media outlets complicates misinformation detection in diverse landscapes like India. This paper introduces a novel multimodal framework combining visual and textual modalities for enhanced fake news detection on Indian media.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
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