# A Graph-Based Context-Aware Model to Understand Online Conversations arXiv: https://arxiv.org/abs/2211.09207 ยท HTML: https://mlsanity.com/paper/2211.09207/ ## Key facts - Cited by 7 papers in the map (data through 14 Oct 2026). - Builds on 22 of its 74 references that are in the map. - First posted on arXiv 16 Nov 2022. ## Builds on (most cited first, 22 of 22) - [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017) - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018) - [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019) - [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016) - [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019) - [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017) - [Heterogeneous Graph Attention Network](https://mlsanity.com/paper/1903.07293/) (2019) - [Measuring and Relieving the Over-smoothing Problem for Graph Neural Networks from the Topological View](https://mlsanity.com/paper/1909.03211/) (2019) - [A Retrospective Analysis of the Fake News Challenge Stance Detection Task](https://mlsanity.com/paper/1806.05180/) (2018) - [SWSR: A Chinese Dataset and Lexicon for Online Sexism Detection](https://mlsanity.com/paper/2108.03070/) (2021) - [A Large-scale Dataset for Hate Speech Detection on Vietnamese Social Media Texts](https://mlsanity.com/paper/2103.11528/) (2021) - [HateCheckHIn: Evaluating Hindi Hate Speech Detection Models](https://mlsanity.com/paper/2205.00328/) (2022) - [GraphNLI: A Graph-based Natural Language Inference Model for Polarity Prediction in Online Debates](https://mlsanity.com/paper/2202.08175/) (2022) - [Vietnamese Hate and Offensive Detection using PhoBERT-CNN and Social Media Streaming Data](https://mlsanity.com/paper/2206.00524/) (2022) - [A Dataset Independent Set of Baselines for Relation Prediction in Argument Mining](https://mlsanity.com/paper/2003.04970/) (2020) - [Identifying Partisan Slant in News Articles and Twitter during Political Crises](https://mlsanity.com/paper/1703.05819/) (2017) - [Tweeting MPs: Digital Engagement between Citizens and Members of Parliament in the UK](https://mlsanity.com/paper/1904.00004/) (2019) - [Illuminating an Ecosystem of Partisan Websites](https://mlsanity.com/paper/1803.03576/) (2018) - [Stop Tracking Me Bro! Differential Tracking Of User Demographics On Hyper-partisan Websites](https://mlsanity.com/paper/2002.00934/) (2020) - [Online Hate: Behavioural Dynamics and Relationship with Misinformation](https://mlsanity.com/paper/2105.14005/) (2021) - [Notes on Abstract Argumentation Theory](https://mlsanity.com/paper/1806.07709/) (2018) - [Under the Spotlight: Web Tracking in Indian Partisan News Websites](https://mlsanity.com/paper/2102.03656/) (2021) ## Built on by (most cited first, 7 of 7) - [AnnoBERT: Effectively Representing Multiple Annotators' Label Choices to Improve Hate Speech Detection](https://mlsanity.com/paper/2212.10405/) (2022) - [AI in the Gray: Exploring Moderation Policies in Dialogic Large Language Models vs. Human Answers in Controversial Topics](https://mlsanity.com/paper/2308.14608/) (2023) - [HateRephrase: Zero- and Few-Shot Reduction of Hate Intensity in Online Posts using Large Language Models](https://mlsanity.com/paper/2310.13985/) (2023) - [Graphically Speaking: Unmasking Abuse in Social Media with Conversation Insights](https://mlsanity.com/paper/2504.01902/) (2025) - [GASCOM: Graph-based Attentive Semantic Context Modeling for Online Conversation Understanding](https://mlsanity.com/paper/2310.14028/) (2023) - [Conversation Kernels: A Flexible Mechanism to Learn Relevant Context for Online Conversation Understanding](https://mlsanity.com/paper/2505.20482/) (2025) - [Leveraging Speech Acts for Low-Data and Cross-Domain Conversation Derailment Forecasting](https://mlsanity.com/paper/2608.25359/) (2026) ## Abstract (arXiv) Online forums that allow for participatory engagement between users have been transformative for the public discussion of many important issues. However, such conversations can sometimes escalate into full-blown exchanges of hate and misinformation. Existing approaches in natural language processing (NLP), such as deep learning models for classification tasks, use as inputs only a single comment or a pair of comments depending upon whether the task concerns the inference of properties of the individual comments or the replies between pairs of comments, respectively. But in online conversations, comments and replies may be based on external context beyond the immediately relevant information that is input to the model. Therefore, being aware of the conversations' surrounding contexts should improve the model's performance for the inference task at hand. We propose GraphNLI, a novel graph-based deep learning architecture that uses graph walks to incorporate the wider context of a conversation in a principled manner. Specifically, a graph walk starts from a given comment and samples "nearby" comments in the same or parallel conversation threads, which results in additional embeddings that are aggregated together with the initial comment's embedding. We then use these enriched embeddings for downstream NLP prediction tasks that are important for online conversations. We evaluate GraphNLI on two such tasks - polarity prediction and misogynistic hate speech detection - and found that our model consistently outperforms all relevant baselines for both tasks. Specifically, GraphNLI with a biased root-seeking random walk performs with a macro-F1 score of 3 and 6 percentage points better than the best-performing BERT-based baselines for the polarity prediction and hate speech detection tasks, respectively.