<![CDATA[In the digital era, social media platforms have become essential tools for communication, content creation, and information dissemination. However, with the increasing volume of user-generated content, the spread of negative or harmful content has emerged as a major challenge for platform administrators and users alike. This study aims to compare TikTok and Instagram in their capacity to detect and manage negative content using Natural Language Processing (NLP) techniques. A dataset of 2,000 user comments was collected—1,000 from each platform—through web scraping. These comments were analyzed using a variety of NLP methods, including sentiment analysis tools (VADER and TextBlob), text classification algorithms (Support Vector Machine and Random Forest), and Named Entity Recognition (NER) using the spaCy library. The comparison was conducted based on the classification performance of each NLP technique in detecting negative content, considering metrics such as accuracy, precision, recall, and F1-score. The results showed that while both SVM and Random Forest performed well in classification tasks, SVM outperformed the others in terms of overall accuracy and consistency across platforms. Sentiment analysis provided a general overview of content polarity, but it was less effective in detecting nuanced or sarcastic language. NER contributed to identifying specific entities that may be associated with negative expressions, enriching the contextual understanding of comments. This study highlights the potential of combining multiple NLP methods to improve automated content moderation systems. It also underlines the importance of platform-specific characteristics, such as user behavior and engagement style, which influence the nature and frequency of negative content. Future work should focus on improving the handling of contextual ambiguity and sarcasm to ensure more robust and adaptive moderation technologies across different social media platforms.]]>
