Papers with Social media platforms
Rumor Detection on Twitter Using Multiloss Hierarchical BiLSTM with an Attenuation Factor (2020.aacl-main)
Copied to clipboard
| Challenge: | Existing models to classify rumors have low precision and are time consuming. |
| Approach: | They propose a multiloss hierarchical biLSTM model with an attenuation factor that can extract deep information from limited quantities of text. |
| Outcome: | The proposed model can extract deep information from limited quantities of text. |
Unsupervised stance detection for arguments from consequences (2020.emnlp-main)
Copied to clipboard
| Challenge: | Social media platforms are becoming an essential venue for online deliberation . stance detection is a task to determine whether a text is in favor of, against, or unrelated to a given topic. |
| Approach: | They propose an unsupervised method to detect the stance of argumentative claims with respect to a topic. |
| Outcome: | The proposed method outperforms BERT and can be comparable to other methods. |
Rumor Detection on Social Media: Datasets, Methods and Opportunities (D19-50)
Copied to clipboard
| Challenge: | Social media platforms are used for information gathering, but they also lead to the spreading of rumors and fake news. |
| Approach: | This paper presents a comprehensive list of datasets used for rumor detection . it also reviews the important studies based on what types of information they exploit . |
| Outcome: | This paper presents an overview of the recent studies in the rumor detection field . it provides a comprehensive list of datasets used for rumour detection . |
SocialForge: simulating the social internet to provide realistic training against influence operations (2025.acl-industry)
Copied to clipboard
Ulysse Oliveri, Guillaume Gadek, Alexandre Dey, Benjamin Costé, Damien Lolive, Arnaud Delhay, Bruno Grilheres
| Challenge: | Social media platforms have enabled large-scale influence campaigns, impacting democratic processes. |
| Approach: | They propose a system to enhance diversity and realism of the generated content while ensuring its adherence to the original scenario. |
| Outcome: | The proposed system improves diversity and realism while ensuring its adherence to the original scenario. |
Tailoring Rumor Debunking to You: Diversifying Chinese Rumor-Debunking Passages with an LLM-Driven Simulated Feedback-Enhanced Framework (2026.eacl-industry)
Copied to clipboard
| Challenge: | Existing methods for fact-checking lack coherence and context, whereas abstractive methods lack cohesion and context. |
| Approach: | They propose a framework that generates Chinese user-specific debunking passages . they propose to use a generative AI framework to generate context-sensitive responses . |
| Outcome: | The proposed framework generates Chinese user-specific debunking passages by iteratively refining outputs based on simulated user feedback. |
The Engage Corpus: A Social Media Dataset for Text-Based Recommender Systems (2022.lrec-1)
Copied to clipboard
| Challenge: | Existing studies have examined the impact of recommendation algorithms on how users discover and join online groups, but there are few standardized datasets for generating such models. |
| Approach: | They propose to use Reddit to build a dataset that can be used to build models of user engagement with online groups. |
| Outcome: | The proposed model is based on the behavior of subreddits banned in June 2020 as part of Reddit's efforts to stop the dissemination of hate speech. |
Harnessing Abstractive Summarization for Fact-Checked Claim Detection (2022.coling-1)
Copied to clipboard
| Challenge: | Social media platforms are becoming battlegrounds for anti-social elements . fact-checking organizations cannot cope with the rapid dissemination of misinformation . a new workflow for fact- checking can be implemented to reduce human time for tasks with high cognition . |
| Approach: | They propose a workflow for detecting previously fact-checked claims that uses abstractive summarization to generate crisp queries. |
| Outcome: | The proposed workflow achieves Recall@5 and MRR of 35% and 0.3, respectively. |
MM-SOC: Benchmarking Multimodal Large Language Models in Social Media Platforms (2024.findings-acl)
Copied to clipboard
| Challenge: | Social media platforms are hubs for multimodal information exchange, encompassing text, images, and videos, making it challenging for machines to comprehend the information or emotions associated with interactions in online spaces. |
| Approach: | They propose a benchmark to evaluate MLLMs' understanding of multimodal social media content and a large-scale YouTube tagging dataset to evaluate their performance. |
| Outcome: | The proposed model performs better in a zero-shot setting, suggesting potential improvements. |
Natural Disaster Tweets Classification Using Multimodal Data (2023.emnlp-main)
Copied to clipboard
| Challenge: | Social media platforms are used for expressing opinions or conveying information. |
| Approach: | They propose a hierarchical system that can integrate multimodal data and perform sequential hierarchic classification. |
| Outcome: | The proposed system can find the damage and its severity along with classify the data into humanitarian categories. |
On the Robustness of Offensive Language Classifiers (2022.acl-long)
Copied to clipboard
| Challenge: | Existing studies on offensive language classifiers have focused on primitive attacks such as misspellings and extraneous spaces. |
| Approach: | They analyze the robustness of offensive language classifiers against crafty adversarial attacks that leverage greedy- and attention-based word selection and context-aware embeddings for word replacement. |
| Outcome: | The proposed classifiers are robust against more crafty attacks that leverage greedy- and attention-based word selection and context-aware embeddings for word replacement. |
BD-SHS: A Benchmark Dataset for Learning to Detect Online Bangla Hate Speech in Different Social Contexts (2022.lrec-1)
Copied to clipboard
Nauros Romim, Mosahed Ahmed, Md Saiful Islam, Arnab Sen Sharma, Hriteshwar Talukder, Mohammad Ruhul Amin
| Challenge: | Social media platforms and online streaming services have spawned a new breed of Hate Speech (HS) due to the massive amount of user-generated content, modern machine learning techniques are feasible and cost-effective to tackle this problem. |
| Approach: | They propose to use a large manually labeled Bangla HS dataset to train generalizable models. |
| Outcome: | The proposed dataset includes more than 50,200 offensive comments crawled from online social networking sites and is at least 60% larger than existing Bangla HS datasets. |
HindiMD: A Multi-domain Corpora for Low-resource Sentiment Analysis (2022.lrec-1)
Copied to clipboard
| Challenge: | Social media platforms such as Twitter and Facebook are a new channel of information dissemination for many negative groups for recruitment. |
| Approach: | They propose to use a social media sentiment analysis corpus annotated with the sentiment classes positive, negative and neutral to investigate the polarity of user-expressed opinions. |
| Outcome: | The proposed model is based on a set of benchmark datasets for sentiment analysis across a range of domains and languages. |
Are We in the AI-Generated Text World Already? Quantifying and Monitoring AIGT on Social Media (2025.acl-long)
Copied to clipboard
| Challenge: | Social media platforms are experiencing a growing presence of AI-Generated Texts (AIGTs) however, the misuse of AIGTs could have profound implications for public opinion . |
| Approach: | They collect a dataset with 2.4M posts from 3 major social media platforms . they then construct a diverse dataset to train and evaluate AIGT detectors . |
| Outcome: | The proposed dataset analyzes 2.4M posts from 3 major social media platforms from 2022 to 2024 . it finds that Medium and Quora show marked increases in AAR . |
PopALM: Popularity-Aligned Language Models for Social Media Trendy Response Prediction (2024.lrec-main)
Copied to clipboard
| Challenge: | Recent work focuses on generic human responses without considering popularity factors in the social contexts. |
| Approach: | They propose Popularity-Aligned Language Models to distinguish responses liked by a larger audience through reinforcement learning. |
| Outcome: | The proposed model can distinguish responses liked by a larger audience through reinforcement learning. |
Social Convos: Capturing Agendas and Emotions on Social Media (2024.lrec-main)
Copied to clipboard
| Challenge: | Social media traffic can provide valuable insights into prevailing opinions and social dynamics among different segments of the population. |
| Approach: | They propose a method to extract influence indicators from messages circulating among groups . they build upon the concept of a convo to identify influential authors . |
| Outcome: | The proposed approach extracts influence indicators from messages circulating among groups of users discussing particular topics. |
Stance Reasoner: Zero-Shot Stance Detection on Social Media with Explicit Reasoning (2024.lrec-main)
Copied to clipboard
| Challenge: | Stance Reasoner is a model for zero-shot stance detection on social media platforms that can be used to extract opinions from opinionated content. |
| Approach: | They propose a method that leverages explicit reasoning over background knowledge to guide the model’s inference about the document’s stance on a target. |
| Outcome: | The proposed model outperforms the current state-of-the-art models on 3 Twitter datasets, including fully supervised models. |
Cause-CSD: A Challenge Multimodal Conversational Stance Cause Detection Dataset and Effective Method (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing stance detection methods treat opinions as surface-level labels, overlooking conversational evidence behind stance expressions. |
| Approach: | They propose a task that jointly identifies stance polarity and contextual evidence . they propose stance-cause Detection language model that leverages explicit context reasoning . |
| Outcome: | The proposed task outperforms baseline methods on text-only and multimodal subtasks. |