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Register a new userWikiTalkEdit: A Dataset for modeling Editors' behaviors on Wikipedia
WikitalkEdit: مجموعة بيانات لسلوكيات تحرير النمذجة على ويكيبيديا
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تقدم هذه الدراسة وتحليلات WikitalkEdit وديجمات من المحادثات وتعديل التواريخ من ويكيبيديا، للبحث في التعاون عبر الإنترنت ونمذجة المحادثة. تضم DataSet ثلاث مرات حوار من صفحات الحديث Wikipedia، وتحرير الإجراءات على المقالات المقابلة التي تتم مناقشتها. نظرا لكيفية دعم البيانات الفهم الكلاسيكي للمطابقة النمط، حيث تتوقع العاطفة الإيجابية واستخدام الضمائر ذات الشخص الأول تغييرا عاطفيا إيجابي في مساهم ويكيبيديا. ومع ذلك، فإنهم لا يتوقعون سلوك التحرير. من ناحية أخرى، فإن ردود الفعل التي تم استدعاء الوكلاء والنقد، والمراجع إلى قواعد المجتمع في ويكيبيديا، من المرجح أن تقنع المساهم في أداء التعديلات ولكنها أقل عرضة للتأدي إلى مشاعر إيجابية. لقد قمنا بتطوير مصنفات أساسية مدربة على ميزات روبرتا مدربة مسبقا والتي يمكن أن تتنبأ بالتغيير التحريري بدرجة F1 من .54، بالمقارنة مع درجة F1 من .66 للتنبؤ بالتغيير العاطفي. كما يتم توفير تحليل تشخيصي للأخطاء الاستمرارية. نستنتج مع التطبيقات والتوصيات المحتملة للعمل في المستقبل. تتوفر DataSet علنا لمجتمع البحث في https://github.com/kj2013/wikitalkedit/.
This study introduces and analyzes WikiTalkEdit, a dataset of conversations and edit histories from Wikipedia, for research in online cooperation and conversation modeling. The dataset comprises dialog triplets from the Wikipedia Talk pages, and editing actions on the corresponding articles being discussed. We show how the data supports the classic understanding of style matching, where positive emotion and the use of first-person pronouns predict a positive emotional change in a Wikipedia contributor. However, they do not predict editorial behavior. On the other hand, feedback invoking evidentiality and criticism, and references to Wikipedia's community norms, is more likely to persuade the contributor to perform edits but is less likely to lead to a positive emotion. We developed baseline classifiers trained on pre-trained RoBERTa features that can predict editorial change with an F1 score of .54, as compared to an F1 score of .66 for predicting emotional change. A diagnostic analysis of persisting errors is also provided. We conclude with possible applications and recommendations for future work. The dataset is publicly available for the research community at https://github.com/kj2013/WikiTalkEdit/.
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