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قل "نعم" إلى الإيجابية: اكتشاف اللغة السامة في مجال الاتصالات
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الاتصالات في مكان العمل (على سبيل المثال البريد الإلكتروني والدردشة، إلخ.) هو جزء أساسي من إنتاجية المؤسسة. المحادثات الصحية أمر حاسم لإنشاء بيئة شاملة والحفاظ على الوئام في منظمة. يمكن للاتصالات السامة في مكان العمل أن تؤثر سلبا على الرضا الوظيفي الإجمالي وغالبا ما تكون خفية أو مخفية أو إظهار تحيزات بشرية. جعلت الدقة اللغوية للمحادثات الخفيفة والأذى من الصعب على الباحثين تحديدها واستخراج المحادثات السامة تلقائيا. في حين أن اللغة الهجومية أو الكلام الكراهية قد درست على نطاق واسع في المجتمعات الاجتماعية، إلا أنه كان هناك القليل من العمل في دراسة الاتصالات السامة في رسائل البريد الإلكتروني. على وجه التحديد، فإن عدم وجود كوربوس، Sparsity من السمية في رسائل البريد الإلكتروني للمؤسسات، ومعايير محددة جيدا للتسجيل المحادثات السامة قد منع الباحثون من معالجة المشكلة على نطاق واسع. نأخذ الخطوة الأولى نحو دراسة السمية في رسائل البريد الإلكتروني في مكان العمل من خلال توفير (1) تصنيفا عاما وقابل للاستثناء بشكل خاص لدراسة اللغة السامة في مكان العمل (2) مجموعة بيانات لدراسة اللغة السامة في مكان العمل بناء على التصنيف و (3) تحليل لماذا لا تكون مجموعات البيانات الهجومية والكراهية مناسبة للكشف عن سمية مكان العمل.
Workplace communication (e.g. email, chat, etc.) is a central part of enterprise productivity. Healthy conversations are crucial for creating an inclusive environment and maintaining harmony in an organization. Toxic communications at the workplace can negatively impact overall job satisfaction and are often subtle, hidden, or demonstrate human biases. The linguistic subtlety of mild yet hurtful conversations has made it difficult for researchers to quantify and extract toxic conversations automatically. While offensive language or hate speech has been extensively studied in social communities, there has been little work studying toxic communication in emails. Specifically, the lack of corpus, sparsity of toxicity in enterprise emails, and well-defined criteria for annotating toxic conversations have prevented researchers from addressing the problem at scale. We take the first step towards studying toxicity in workplace emails by providing (1) a general and computationally viable taxonomy to study toxic language at the workplace (2) a dataset to study toxic language at the workplace based on the taxonomy and (3) analysis on why offensive language and hate-speech datasets are not suitable to detect workplace toxicity.
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Automatic detection of toxic language plays an essential role in protecting social media users, especially minority groups, from verbal abuse. However, biases toward some attributes, including gender, race, and dialect, exist in most training dataset
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