Social media has become a major driver of social change, by facilitating the
formation of online social movements. Automatically understanding the
perspectives driving the movement and the voices opposing it, is a challenging
task as annotated data is difficult to obtain. We propose a weakly supervised
graph-based approach that explicitly models perspectives in
#BackLivesMatter-related tweets. Our proposed approach utilizes a
social-linguistic representation of the data. We convert the text to a graph by
breaking it into structured elements and connect it with the social network of
authors, then structured prediction is done over the elements for identifying
perspectives. Our approach uses a small seed set of labeled examples. We
experiment with large language models for generating artificial training
examples, compare them to manual annotation, and find that it achieves
comparable performance. We perform quantitative and qualitative analyses using
a human-annotated test set. Our model outperforms multitask baselines by a
large margin, successfully characterizing the perspectives supporting and
opposing #BLM.

社交媒体成为社会变革的重要推动力，通过促进在线社会运动的形成。我们提出了一种基于弱监督图模型的方法，明确地建模了与 #BackLivesMatter 相关的推文中的观点。我们的方法利用了数据的社会 - 语言学表示，通过将文本分解为结构化元素并将其与作者的社交网络连接来将其转化为图形，然后在元素上进行结构化预测以识别观点。我们的方法利用了少量有标签的样本集。我们尝试使用大型语言模型生成人工训练样本，并将其与手动注释进行比较，发现其性能达到了可比较的水平。我们使用人工注释的测试集进行定量和定性分析。我们的模型在定性和定量分析中优于多任务基准，成功地描述了支持和反对 #BLM 的观点。

「两种运动的故事」：使用弱监督基于图的结构化预测方法识别和比较 #BlackLivesMatter 和 #BlueLivesMatter 运动相关推文中的观点

"A Tale of Two Movements": Identifying and Comparing Perspectives in  #BlackLivesMatter and #BlueLivesMatter Movements-related Tweets using Weakly  Supervised Graph-based Structured Prediction