اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدتصحيح اتجاهات الرأي العام من خلال التكامل البيزي
Correcting public opinion trends through Bayesian data assimilation
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نشر من قبل
Rossella Arcucci Dr
تاريخ النشر
2021
مجال البحث
الهندسة المعلوماتية
والبحث باللغة
English
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إن قياس الرأي العام هو التركيز الرئيسي خلال الانتخابات الديمقراطية، مما يتيح للمرشحين تقييم شعبيتهم وتعديل استراتيجيات حملاتهم وفقا لذلك. يبقى التحليل المسحي التقليدي هو الطريقة الأكثر شعبية للتقدير، على الرغم من تكاليفه وزمنه الطويل وأخطاء القياس وعدم وجود القدرة على التحدث بشكل مباشر والتمثيل المتأخر للرأي العام. في السنوات الأخيرة، حاولت تعدين الرأي عبر تويتر تجاوز هذه المشاكل. على الرغم من تحقيق نتائج مثيرة للإعجاب، فإنه يواجه مجموعة من العيوب مثل عينة غير ممثلة وعدم وجود ثبات طويل الأجل. يهدف هذا البحث إلى دمج البيانات من كلا هذين التقنيتين باستخدام التحليل البيزي للحصول على تقدير أكثر دقة للرأي الحقيقي لاستفتاء البريطانيا. يثبت هذا البحث فعالية الطريقة المقترحة باستخدام بيانات الرأي عبر تويتر وبيانات التحليل المسحي من المسحيون الموثوق بهم. أولا، يتم التعرف على احتمال وجود فجوة زمنية بين البيانات المختلفة بقيمة 16 يوما. ويتم تضمين هذه الفجوة في الهيكل المقترح للتحليل. وتم العثور على أن هذه الطريقة تضمن بشكل كاف تضمين المعلومات من كلا المصادر وقياس اتجاه قوي نحو دعم الخروج قبل استفتاء البريطانيا. وتوفر الطريقة المقترحة تقديرات مفيدة للرأي الحقيقي، والتي تعد ضرورية للبحوث المستقبلية في قياس وتنبؤ الرأي العام.
Measuring public opinion is a key focus during democratic elections, enabling candidates to gauge their popularity and alter their campaign strategies accordingly. Traditional survey polling remains the most popular estimation technique, despite its cost and time intensity, measurement errors, lack of real-time capabilities and lagged representation of public opinion. In recent years, Twitter opinion mining has attempted to combat these issues. Despite achieving promising results, it experiences its own set of shortcomings such as an unrepresentative sample population and a lack of long term stability. This paper aims to merge data from both these techniques using Bayesian data assimilation to arrive at a more accurate estimate of true public opinion for the Brexit referendum. This paper demonstrates the effectiveness of the proposed approach using Twitter opinion data and survey data from trusted pollsters. Firstly, the possible existence of a time gap of 16 days between the two data sets is identified. This gap is subsequently incorporated into a proposed assimilation architecture. This method was found to adequately incorporate information from both sources and measure a strong upward trend in Leave support leading up to the Brexit referendum. The proposed technique provides useful estimates of true opinion, which is essential to future opinion measurement and forecasting research.
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