“A Tale of Two Movements’: Identifying and Comparing Perspectives in #BlackLivesMatter and #BlueLivesMatter Movements-related Tweets using Weakly Supervised Graph-based Structured Prediction (2023.findings-emnlp)
Copied to clipboard
| Challenge: | a weakly supervised graph-based approach to model #BLM-related tweets is difficult to obtain . |
| Approach: | They propose a weakly supervised graph-based approach that explicitly models perspectives in #BackLivesMatter-related tweets. |
| Outcome: | The proposed model outperforms multitask baselines by a large margin. |
Similar Papers
Detecting and Reducing Bias in a High Stakes Domain (D19-1)
Copied to clipboard
| Challenge: | Existing research shows that a deep learning model can predict aggression and loss in posts by focusing on stop words such as “a” or “on”. |
| Approach: | They developed an approach to interpret a deep learning model that often bases its predictions on stop words such as "a" or "on" to tackle bias, they annotated the rationales and built models that drastically reduce bias. |
| Outcome: | The proposed model can predict aggression and loss in posts by using stop words such as "a" or "on" the new annotations enable us to quantitatively measure how justified the model predictions are, and build models that drastically reduce bias. |
Using Social and Linguistic Information to Adapt Pretrained Representations for Political Perspective Identification (2021.findings-acl)
Copied to clipboard
| Challenge: | a new framework for political perspective detection is proposed to improve text training costs . current deep learning models lack the ability to focus on text span for bias detection . |
| Approach: | They propose a framework that pretrains the text model using social and linguistic contexts . they demonstrate that the framework improves performance by identifying bias-related text spans based on entity mentions and news sharing . |
| Outcome: | The proposed framework improves on two news bias datasets and improves performance on the general source and task. |
Speak up, Fight Back! Detection of Social Media Disclosures of Sexual Harassment (N19-3)
Copied to clipboard
| Challenge: | #MeToo movement provides platform to narrate personal experiences of sexual harassment. |
| Approach: | They propose a three-part ULMFiT architecture to tackle text subtleties in a classification task . they propose to annotate a manually annotated real-world dataset to test their approach . |
| Outcome: | The proposed model outperforms existing models that rely on handcrafted stylistic features and is more accurate than generic models. |
Modeling Ideological Salience and Framing in Polarized Online Groups with Graph Neural Networks and Structured Sparsity (2022.findings-naacl)
Copied to clipboard
| Challenge: | Existing methods to detect ideological divides in social media rely on knowing in advance the political orientation of text . fascist and mainstream are among the most polarized concepts in reddit in 2019 . |
| Approach: | They propose a minimally supervised method that leverages the network structure of online discussion forums to detect polarized concepts. |
| Outcome: | The proposed framework captures temporal ideological dynamics such as right-wing and left-wing radicalization using graph neural networks and sparsity learning. |
Analyzing the Dynamics of Climate Change Discourse on Twitter: A New Annotated Corpus and Multi-Aspect Classification (2024.lrec-main)
Copied to clipboard
Shuvam Shiwakoti, Surendrabikram Thapa, Kritesh Rauniyar, Akshyat Shah, Aashish Bhandari, Usman Naseem
| Challenge: | a lack of data on climate change discourse has highlighted the need for further advancement . a new study examines the discourse on social media platforms that ignores climate change . |
| Approach: | They analyze climate change discourse on Twitter using a meticulously annotated dataset . they find relevance, stance, hate speech, direction of hate, humor and humor are key aspects . |
| Outcome: | The proposed method combines annotated tweets with a dataset of 15,309 tweets . it reveals tweet distribution patterns, stance prevalence, and hate speech trends . |
Deep Attentive Learning for Stock Movement Prediction From Social Media Text and Company Correlations (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing models that predict stock movements are based on time series and technical analysis, but price signals alone fail to capture market surprises and impacts of sudden unexpected events. |
| Approach: | They propose a model that integrates chaotic temporal signals from financial data and social media to create hierarchical temporal networks. |
| Outcome: | The proposed model can be used to forecast stock movements on real-world S&P 500 index data and English tweets. |
Unveiling the Truth and Facilitating Change: Towards Agent-based Large-scale Social Movement Simulation (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for simulating social movements encounter challenges in capturing behavior of participants. |
| Approach: | They propose a hybrid framework for social media user simulation wherein users are categorized into two types: core and ordinary users. |
| Outcome: | The proposed framework is able to simulate the behavior of social media users across real-world datasets and demonstrate its effectiveness and flexibility. |
Detecting Sexism in Tweets: A Sentiment Analysis and Graph Neural Network Approach (2025.naacl-srw)
Copied to clipboard
Diana P. Madera-Espíndola, Zoe Caballero-Domínguez, Valeria J. Ramírez-Macías, Sabur Butt, Hector Ceballos
| Challenge: | a new tool to detect sexism on social media platforms is being developed to identify such behavior . sexist ideologies such as sextism and gender-based violence can be spread through social media . |
| Approach: | They propose to use BERT and GraphSAGE to analyze tweets for sexism detection . they also use sentiment analysis and natural language processing techniques to classify tweets . |
| Outcome: | The proposed tool analyzes tweets for sexism detection and classifies them into five categories. |
Predicting the Topical Stance and Political Leaning of Media using Tweets (2020.acl-main)
Copied to clipboard
| Challenge: | Existing methods for determining stances of media outlets and influential people are expensive. |
| Approach: | They propose a method that uses unsupervised learning to ascertain the stance of Twitter users with respect to a polarizing topic by leveraging their retweet behavior. |
| Outcome: | The proposed method achieves 82.6% accuracy compared to gold labels from the Media Bias/Fact Check website . |
Understanding Emotions: A Dataset of Tweets to Study Interactions between Affect Categories (L18-1)
Copied to clipboard
| Challenge: | a new dataset is used to classify text into positive, negative, and neutral classes . a large amount of work on automatic detecting emotions from text has focused on classifying text into basic emotion categories . |
| Approach: | They use Twitter as the source of the textual data they annotate to find out which emotions often present together in tweets . |
| Outcome: | The proposed dataset is useful for training and testing supervised machine learning algorithms . it is based on the results of the SemEval-2018 task 1: Affect in Tweets . |