Paper Reading AI Learner

Online influence, offline violence: Linguistic responses to the 'Unite the Right' rally

2019-08-30 08:58:11
Isabelle van der Vegt, Maximilian Mozes, Paul Gill, Bennett Kleinberg

Abstract

The media frequently describes the 2017 Charlottesville 'Unite the Right' rally as a turning point for the alt-right and white supremacist movements. Related research into social movements also suggests that the media attention and public discourse concerning the rally may have influenced the alt-right. Empirical evidence for these claims is largely lacking. The current study investigates potential effects of the rally by examining a dataset of 7,142 YouTube video transcripts from alt-right and progressive channels. We examine sentiment surrounding the ten most frequent keywords (single words and word pairs) in transcripts from each group, eight weeks before to eight weeks after the rally. In the majority of cases, no significant differences in sentiment were found within and between the alt-right and progressive groups, both pre- and post-Charlottesville. However, we did observe more negative sentiment trends surrounding 'Bernie Sanders' and 'black people' in the alt-right and progressive groups, respectively. We also observed more negative sentiment after the rally regarding 'Democratic Party' in the alt-right videos compared to the progressive videos. We suggest that the observed results potentially reflect minor changes in political sentiment before and after the rally, as well as differences in political sentiment between the alt-right and progressive groups in general.

Abstract (translated)

URL

https://arxiv.org/abs/1908.11599

PDF

https://arxiv.org/pdf/1908.11599.pdf


Tags
3D Action Action_Localization Action_Recognition Activity Adversarial Agent Attention Autonomous Bert Boundary_Detection Caption Chat Classification CNN Compressive_Sensing Contour Contrastive_Learning Deep_Learning Denoising Detection Dialog Diffusion Drone Dynamic_Memory_Network Edge_Detection Embedding Embodied Emotion Enhancement Face Face_Detection Face_Recognition Facial_Landmark Few-Shot Gait_Recognition GAN Gaze_Estimation Gesture Gradient_Descent Handwriting Human_Parsing Image_Caption Image_Classification Image_Compression Image_Enhancement Image_Generation Image_Matting Image_Retrieval Inference Inpainting Intelligent_Chip Knowledge Knowledge_Graph Language_Model Matching Medical Memory_Networks Multi_Modal Multi_Task NAS NMT Object_Detection Object_Tracking OCR Ontology Optical_Character Optical_Flow Optimization Person_Re-identification Point_Cloud Portrait_Generation Pose Pose_Estimation Prediction QA Quantitative Quantitative_Finance Quantization Re-identification Recognition Recommendation Reconstruction Regularization Reinforcement_Learning Relation Relation_Extraction Represenation Represenation_Learning Restoration Review RNN Salient Scene_Classification Scene_Generation Scene_Parsing Scene_Text Segmentation Self-Supervised Semantic_Instance_Segmentation Semantic_Segmentation Semi_Global Semi_Supervised Sence_graph Sentiment Sentiment_Classification Sketch SLAM Sparse Speech Speech_Recognition Style_Transfer Summarization Super_Resolution Surveillance Survey Text_Classification Text_Generation Tracking Transfer_Learning Transformer Unsupervised Video_Caption Video_Classification Video_Indexing Video_Prediction Video_Retrieval Visual_Relation VQA Weakly_Supervised Zero-Shot