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Register a new user(Mis)alignment Between Stance Expressed in Social Media Data and Public Opinion Surveys
(MIS) محاذاة بين الموقف المعبر عنه في بيانات وسائل التواصل الاجتماعي ومسوحات الرأي العام
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الاكتشاف الموقف، الذي يهدف إلى تحديد ما إذا كان الفرد هو مفهوم مستهدف أو ضد الكشف عن الرأي العام من تدفقات بيانات وسائل التواصل الاجتماعي الكبيرة. ومع ذلك، فإن التعليق البشري لمحتوى وسائل التواصل الاجتماعي لا يستوفي دائما الموقف "" كما يقاس من خلال استطلاعات الرأي العام. نوضح ذلك من خلال مقارنة موقف الفرد المبلغ عنها مباشرة إلى الموقف الذي استنتج من بيانات وسائل التواصل الاجتماعي الخاصة بهم. استفاد من استطلاع للرأي العام الطولي مع مقاعد Twitter المستفتى، أجرينا هذه المقارنة مع 1،129 فردا في أربع أهداف بارزة. نجد أن الاستدعاء مرتفعا لكل من تصنيفات الموقف المحترفين، ولكن الدقة متغير في عدد من الحالات. نحدد ثلاثة عوامل تؤدي إلى قطع الاتصال بين النص وموقف المؤلف: التناقضات الزمنية والاختلافات في البنيات وأخطاء القياس من كل من المشاركين في المسح والمعجبين. من خلال تقديم إطار لتقييم حدود نماذج الكشف عن الموقف، يوفر هذا العمل نظرة مهمة في الكشف عن الموقف حقا.
Stance detection, which aims to determine whether an individual is for or against a target concept, promises to uncover public opinion from large streams of social media data. Yet even human annotation of social media content does not always capture stance'' as measured by public opinion polls. We demonstrate this by directly comparing an individual's self-reported stance to the stance inferred from their social media data. Leveraging a longitudinal public opinion survey with respondent Twitter handles, we conducted this comparison for 1,129 individuals across four salient targets. We find that recall is high for both Pro'' and Anti'' stance classifications but precision is variable in a number of cases. We identify three factors leading to the disconnect between text and author stance: temporal inconsistencies, differences in constructs, and measurement errors from both survey respondents and annotators. By presenting a framework for assessing the limitations of stance detection models, this work provides important insight into what stance detection truly measures.
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