Papers with Instagram
Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing (2020.aacl-main)
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| Challenge: | asian-pacific chapter of AACL is hosting its first conference in 2020 . a face-to-face physical meeting would have been eye-opening to participants . |
| Approach: | ai chiang is the General Chair of the Asia-Pacific Chapter of the Association for Computational Linguistics . he is also the General chair of the 10th International Joint Conference on Natural Language Processing . |
| Outcome: | the 1st Asia-Pacific Chapter of the Association for Computational Linguistics will hold its annual conference in 2020 . the conference will be held in conjunction with the 10th International Joint Conference on Natural Language Processing . |
ScamSpot: Fighting Financial Fraud in Instagram Comments (2024.eacl-demo)
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| Challenge: | Existing research on spam and scams on Instagram is limited to theoretical concepts and only a recall of 11.51%. |
| Approach: | They propose a system that includes a browser extension, a fine-tuned BERT model and a REST API to solve the problem of spam and fraudulent messages in the comment sections of Instagram pages. |
| Outcome: | The proposed system includes a browser extension, a fine-tuned BERT model and a REST API. |
Bridging Modality Gap for Effective Multimodal Sentiment Analysis in Fashion-related Social Media (2025.coling-main)
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| Challenge: | Existing sentiment analysis tasks focus on text comprehension, but visual content is important for emotional expression. |
| Approach: | They propose a multimodal framework that integrates information from various modalities for sentiment classification of fashion posts. |
| Outcome: | The proposed framework outperforms existing unimodal and multimodal baselines on a comprehensive dataset and significantly outperformed existing unilmodal and multiple modal frameworks. |
Mitigating Bias in Session-based Cyberbullying Detection: A Non-Compromising Approach (2021.acl-long)
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| Challenge: | Existing efforts to enhance the performance of session-based cyberbullying detection have overlooked unintended social biases in existing datasets. |
| Approach: | They propose a model-agnostic debiasing strategy that leverages a reinforcement learning technique to mitigate unintended biases in existing datasets. |
| Outcome: | The proposed approach can mitigate unintended biases without impairing the detection performance. |
Visual Attention Model for Name Tagging in Multimodal Social Media (P18-1)
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| Challenge: | Name tagging is a key task for language understanding, but is often limited by the short textual components. |
| Approach: | They propose a novel model architecture based on visual attention that outperforms other methods . they use multimodal datasets to analyze the name tagging task on social media . |
| Outcome: | The proposed model outperforms existing methods and significantly outperformed existing methods. |
Hope ‘The Paragraph Guy’ explains the rest : Introducing MeSum, the Meme Summarizer (2024.findings-emnlp)
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| Challenge: | a lack of large datasets for supervised learning and resource-intensive vision language models have hindered the development of meme comprehension. |
| Approach: | They propose a framework to bridge the gap between meme comprehension and vision language models by using a multimodal dataset. |
| Outcome: | The proposed framework outperforms existing methods in the meme comprehension test. |
HateBR: A Large Expert Annotated Corpus of Brazilian Instagram Comments for Offensive Language and Hate Speech Detection (2022.lrec-1)
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| Challenge: | In Brazil, hate speech is prohibited, however the regulation is not effective due to the difficulty of identifying, quantifying and classifying this kind of online content. |
| Approach: | They propose to annotate a large corpus of Brazilian Instagram comments manually and to use it to detect hate speech and offensive language. |
| Outcome: | The HateBR corpus was collected from the comment section of Brazilian politicians’ accounts on Instagram and manually annotated by specialists, reaching a high inter-annotator agreement. |
Leveraging Hashtag Networks for Multimodal Popularity Prediction of Instagram Posts (2022.lrec-1)
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| Challenge: | Existing popularity prediction approaches reduce hashtags to simple features such as hashtag length or number of hashtags in a post. |
| Approach: | They propose a multimodal framework to predict popular influencer posts on Instagram using post captions, image, hashtag network and topic model. |
| Outcome: | The proposed framework outperforms baseline models and unimodal models on popular influencer posts in Taiwan . it uses post captions, image, hashtag network, and topic model to predict popular influence post . |
Mapping Toxic Comments Across Demographics: A Dataset from German Public Broadcasting (2025.emnlp-main)
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| Challenge: | Existing toxic speech datasets lack demographic context and age data are limited . funk and its subsidiary accounts target users aged 14-29 . |
| Approach: | a german project introduces a large-scale toxic speech dataset annotated for toxicity . the dataset includes 3,024 human-annotated and 30,024 LLM-annnotated comments . researchers used human expertise and state-of-the-art language models to label comments based on toxic keywords . |
| Outcome: | The study combines human expertise with state-of-the-art language models to identify toxic speech categories. |