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Register a new userWMDecompose: A Framework for Leveraging the Interpretable Properties of Word Mover's Distance in Sociocultural Analysis
WMDECompose: إطارا للاستفادة من الخصائص القابلة للتفسير لمسافة Word Mover في التحليل الاجتماعي الثقافي
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على الرغم من أن شعبية متزايدة من NLP في العلوم الإنسانية والعلوم الاجتماعية، فقد ترافق التقدم في الأداء النموذجي وتعقيد مخاوف بشأن التفسير والسلطة التوضيحية للتحليل الاجتماعي الثقافي. نموذج شعبي واحد يأخذ طريقا وسط مسافة كلمة المحرك (WMD). يتم تكييفها ظاهريا لتفسيرها، ومع ذلك تم استخدام WMD وتم تطويره بشكل أكبر بطرق تجاهل الجانب الأكثر تفسيرا في كثير من الأحيان: أي مسافات مستوى الكلمات المطلوبة لترجمة مجموعة من الكلمات إلى مجموعة أخرى من الكلمات. لمعالجة هذه الفجوة الواضحة، نقدم WMDECOMPOOPE: مكتبة نموذجية ومكتبة بيثون 1) تتحلل مسافات مستوى المستند في المسافات في مستوياتها المكونة على مستوى الكلمات، و 2) مجموعات في وقت لاحق من تحفيز العناصر المواضيعية، بحيث يتم الاحتفاظ بالمعلومات المعجمية المفيدة تلخيص للتحليل. لتوضيح إمكاناتها في سياق علمي اجتماعي، نطبقها على جثة وسائل التواصل الاجتماعي الطولية لاستكشاف العلاقة المتبادلة بين نظريات المؤامرة والأحرفات الأمريكية المحافظة. أخيرا، نظرا لتعقيد الوقت الكامل في الوقت الحالي، فإننا نقترح بالإضافة إلى طريقة لأخذ عينات من مجموعات البيانات الكبيرة بطريقة استنساخ، مع حدود ضيقة تمنع استقراء النتائج غير الموثوقة بسبب سوء أخذ العينات الممارسات.
Despite the increasing popularity of NLP in the humanities and social sciences, advances in model performance and complexity have been accompanied by concerns about interpretability and explanatory power for sociocultural analysis. One popular model that takes a middle road is Word Mover's Distance (WMD). Ostensibly adapted for its interpretability, WMD has nonetheless been used and further developed in ways which frequently discard its most interpretable aspect: namely, the word-level distances required for translating a set of words into another set of words. To address this apparent gap, we introduce WMDecompose: a model and Python library that 1) decomposes document-level distances into their constituent word-level distances, and 2) subsequently clusters words to induce thematic elements, such that useful lexical information is retained and summarized for analysis. To illustrate its potential in a social scientific context, we apply it to a longitudinal social media corpus to explore the interrelationship between conspiracy theories and conservative American discourses. Finally, because of the full WMD model's high time-complexity, we additionally suggest a method of sampling document pairs from large datasets in a reproducible way, with tight bounds that prevent extrapolation of unreliable results due to poor sampling practices.
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