This is your work, valued

Firoj Alam, Scientist, QCRI

Elite
@firojalam

Research interests: NLP, Speech and Machine learning; Disinformation, Emotion, Sentiment, Social media, Humanitarian Informatics, Bangla NLP

multimodal_social_media. multimodal social media content (text, image) classification

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harmful-memes-detection-resources. Resources (conference/journal publications, references to dataset) for harmful memes detection.

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crisis_datasets_benchmarks. Crisis Dataset for Benchmarks Experiments

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medic. Multi-Task Learning for Disaster Image Classification using MEDIC

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domain-adaptation. Domain Adaptation with Adversarial Training and Graph Embeddings

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COVID-19-tweets-for-check-worthiness. COVID-19 Infodemic Twitter dataset

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COVID-19-disinformation. Dataset: Fighting the COVID-19 Infodemic: Modeling the Perspective of Journalists, Fact-Checkers, Social Media Platforms, Policy Makers, and the Society

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LlamaLens. This repository contains the resources, code, and documentation for LlamaLens, a specialized multilingual large language model (LLM) designed to analyze news and social media content effectively. LlamaLens supports multiple languages, including Arabic, English, and Hindi, and is tailored for diverse tasks such as sentiment analysis, misinformation.

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openSMILE-configuration. openSMILE configuration files for the acoustic feature extraction of emotion recognition.

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Detecting-Previously-Fact-Checked-Claims. Role of Context in Detecting Previously Fact-Checked Claims

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crisis-embedding-models. Embedding models designed using crisis related tweets collected by AIDR (http://aidr.qcri.org/)

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covid19-infodemic-demo. API Services: Fighting the COVID-19 Infodemic: Modeling the Perspective of Journalists, Fact-Checkers, Social Media Platforms, Policy Makers, and the Society

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QCRI-Automatic-Report-Generator-for-Disaster-Events-ARGDE. JavaScript

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assisting-fact-checking. Assisting the Human Fact-Checkers: Detecting All Previously Fact-Checked Claims in a Document

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nlp. :memo: This repository recorded my NLP journey.

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nativqa-framework. NativQA Framework collects multilingual, culturally aligned natural queries and QA pairs from real-world local search, making it easy to build region-specific QA datasets for LLM evaluation and tuning.

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TAADpapers. Must-read Papers on Textual Adversarial Attack and Defense

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LLMeBench. Benchmarking Large Language Models

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