跨语言定位信息缺口与叙事不一致性：以维基百科上LGBT群体形象呈现为例的案例研究
Locating Information Gaps and Narrative Inconsistencies Across Languages: A Case Study of LGBT People Portrayals on Wikipedia
AI总结：
本研究提出InfoGap方法，可在事实层面高效定位跨语言文章的信息缺口与叙事不一致性，通过对英、俄、法三语维基百科2700个LGBT人物传记的分析验证其有效性，为大规模精细化跨语言比较分析奠定新基础。
摘要：
为解释社会现象并识别系统性偏差，计算社会科学领域的大量研究聚焦于比较文本分析。这些研究通常依赖粗粒度的语料库层面统计或局部词层面分析，且主要以英文为研究对象。我们提出InfoGap方法——一种在事实层面定位跨语言文章中的信息缺口与不一致性的高效可靠方法。我们通过分析英文、俄文和法文维基百科上2700个传记页面中LGBT群体的形象呈现，对InfoGap进行评估。我们发现不同语言在事实覆盖范围上存在巨大差异。此外，我们的分析显示，带有负面含义的传记事实在俄文维基百科中更有可能被突出强调。至关重要的是，InfoGap既有助于开展大规模分析，又能精准定位局部文档层面和事实层面的信息缺口，为大规模开展有针对性的、细致的跨语言比较分析奠定了新基础。
英文摘要：
To explain social phenomena and identify systematic biases, much research in computational social science focuses on comparative text analyses. These studies often rely on coarse corpus-level statistics or local word-level analyses, mainly in English. We introduce the InfoGap method -- an efficient and reliable approach to locating information gaps and inconsistencies in articles at the fact level, across languages. We evaluate InfoGap by analyzing LGBT people's portrayals, across 2.7K biography pages on English, Russian, and French Wikipedias. We find large discrepancies in factual coverage across the languages. Moreover, our analysis reveals that biographical facts carrying negative connotations are more likely to be highlighted in Russian Wikipedia. Crucially, InfoGap both facilitates large scale analyses, and pinpoints local document- and fact-level information gaps, laying a new foundation for targeted and nuanced comparative language analysis at scale.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
