区分网络言论中针对不同对象的正负情感，揭示隐含的社会立场。
Directed Social Regard: Surfacing Targeted Advocacy, Opposition, Aid, Harms, and Victimization in Online Media
- 用双模型识别文本中情感指向的具体目标
- 在六组第三方数据上验证，结果与社会科学研究标签高度相关
- 适合研究网络舆论、政治话语和心理操控的学者使用
在线平台、影响力操作和政治话语中的语言常在同一句话中同时表达亲社会情感（如支持、帮助、同情）和反社会情感（如威胁、反对、责备），但现有自然语言处理工具仅能判断整体情绪正负，无法识别情感的多重共存及具体指向。本文提出定向社会关注（Directed Social Regard, DSR）方法，采用双阶段变压器模型：首先检测文本中情感的细粒度目标范围，再基于社会心理学中的道德解离与道德框架理论，在三个(-1,1)轴上对各片段进行多维评分。研究构建了DSR数据集的采集与标注策略，设计了跨度级评分的变压器架构，并通过验证实验获得良好效果。将经过验证的DSR模型应用于六个第三方在线媒体数据集，发现其输出与已有社会科学研究数据中的标签和主题存在显著相关性。
原文摘要 · Abstract (English)
The language in online platforms, influence operations, and political rhetoric frequently directs a mix of pro-social sentiment (e.g., advocacy, helpfulness, compassion) and anti-social sentiment (e.g., threats, opposition, blame) at different topics, all in the same message. While many natural language processing (NLP) tools classify or score a text's overall sentiment as positive, neutral, or negative, these tools cannot report that positive and negative sentiments coexist, and they cannot report the target of those sentiments. This paper presents the Directed Social Regard (DSR) approach to multi-dimensional, multi-valence sentiment analysis, comprised of a pair of transformer-based models that (1) detects span-level targets of sentiment in a message and then (2) scores all spans within the message context along three (-1, 1) axes of regard that are motivated by social science theories of moral disengagement and moral framing. We present a data collection and annotation strategy for DSR dataset construction, a transformer-based architecture for span-level scoring, and a validation study with promising results. We apply the validated DSR model on six third-party datasets of online media and report meaningful correlations between DSR outputs and the labels and topics in these pre-existing social science datasets.
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