Papers with online communication
The Evolution of Gen Alpha Slang: Linguistic Patterns and AI Translation Challenges (2025.acl-srw)
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| Challenge: | Generation Alpha (born 2010-2024) exhibits unique linguistic behaviours influenced by rampant online communication and platform-specific cultures. |
| Approach: | They construct a comprehensive slang corpus from online platforms and evaluate four AI translation systems on over 100 sling terms. |
| Outcome: | The proposed translation systems outperform four existing translation models on over 100 slang terms. |
Investigating Human Values in Online Communities (2025.naacl-long)
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| Challenge: | Existing value frameworks struggle with sample sizes and rely on selfreported surveys to calculate values. |
| Approach: | They propose a method to computationally analyse values on Reddit using in-domain and out-of-domain human annotations to train a value relevance and a polarity classifier. |
| Outcome: | The proposed method can be used to analyse values on reddit using human annotations and human annotation. |
Assessing the Human Likeness of AI-Generated Counterspeech (2025.coling-main)
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| Challenge: | Existing studies have focused on relevance, surface form, and other shallow linguistic characteristics. |
| Approach: | They propose to evaluate the human likeness of AI-generated counterspeech . they implement and evaluate several LLM-based generation strategies . |
| Outcome: | The proposed models show that human-written counterspeech can be distinguished by both simple classifiers and humans. |
Selecting Stickers in Open-Domain Dialogue through Multitask Learning (2022.findings-acl)
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| Challenge: | Existing methods to select appropriate stickers in open-domain dialogues have not been explored. |
| Approach: | They propose a multitask learning method consisting of three auxiliary tasks to combine multimodal information to enhance the understanding of dialogue history, emotion and semantic meaning of stickers. |
| Outcome: | The proposed model can combine multimodal information and achieve significantly higher accuracy over strong baselines. |
Linguistic Complexity Loss in Text-Based Therapy (2021.naacl-main)
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| Challenge: | linguistic complexity loss in text-based therapy can be used to identify patterns of mental health . authors: clients who reported more anxiety used less lexically diverse language . |
| Approach: | They analyze linguistic complexity loss in online therapy conversations as it relates to mental health . they find that clients used less lexically diverse language when they were more anxious . |
| Outcome: | The proposed analysis shows that therapists use more complex language when clients are anxious . the authors show that analyzing linguistic complexity can identify meaningful patterns in mental health . |
Joint Modelling of Emotion and Abusive Language Detection (2020.acl-main)
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| Challenge: | Existing methods for abuse detection focus on linguistic properties of comments and online communities of users, disregarding the emotional state of the users and how this might affect their language. |
| Approach: | They propose to combine emotion and abusive language detection to create a multi-task learning framework that allows one task to inform the other. |
| Outcome: | The proposed model improves on the previous models, incorporating affective features into the learning framework. |
Automated Detection of Tropes In Short Texts (2025.coling-main)
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| Challenge: | Tropes are often used in movies to convey familiar patterns, but they also play a significant role in online communication . |
| Approach: | They propose to automatically detect tropes in social media posts by using a dataset . they define the task, distinguish it from previous work, and develop a machine learning technique . |
| Outcome: | The proposed method can detect tropes in social media posts with high accuracy. |
ToxVidLM: A Multimodal Framework for Toxicity Detection in Code-Mixed Videos (2024.findings-acl)
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| Challenge: | Using a dataset of 931 videos with 4021 code-mixed Hindi-English utterances, we find that video content with multiple modalities is more accurate and more accurate than textual content. |
| Approach: | They propose to use a dataset to analyze toxic content in video content in non-English languages by leveraging language models. |
| Outcome: | The proposed framework achieves an Accuracy and Weighted F1 score of 94.29% and 94.35% for the first time in its class. |
The Overlooked Repetitive Lengthening Form in Sentiment Analysis (2024.findings-emnlp)
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| Challenge: | Existing studies have focused on emphatic and expressive language models with informal styles, such as memes and emojis. |
| Approach: | They propose a two-stage Explainable Instruction Tuning framework that can improve LLMs' performance and explainability for RLF with limited samples. |
| Outcome: | The proposed framework can match zero-shot GPT-4 in performance but not explainability for RLF with limited samples. |
GenEx: A Commonsense-aware Unified Generative Framework for Explainable Cyberbullying Detection (2023.emnlp-main)
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| Challenge: | a significant gap exists in understanding code-mixed languages and the need for explainability in this context. |
| Approach: | They propose to annotate posts with four labels to identify bullies in code-mixed languages . they propose to use a generative framework to reimagine the multitask problem as a text-to-text generation task. |
| Outcome: | The proposed model outperforms baseline models and state-of-the-art models on the BullyExplain dataset. |
Comparing a BERT Classifier and a GPT classifier for Detecting Connective Language Across Multiple Social Media (2024.emnlp-main)
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| Challenge: | Using social media, researchers have built a variety of text classifiers to understand short-form text . however, there is little discussion regarding what desired language on social media would look like . |
| Approach: | They propose an approach for detecting connective language from social media discussions using BERT and GPT-3.5 turbo. |
| Outcome: | The proposed classifier outperforms the existing classifiers in detecting connective language from social media discussions. |