Sentiment Analysis: Sentiment analysis involves using machine learning algorithms to automatically classify the sentiment expressed in social media content. This method can help identify whether the attitudes expressed by social media users are positive, negative, or neutral, and can provide insight into the emotional tone of the content. Sentiment analysis can be useful for identifying patterns in how users feel about certain topics, products, or brands.

Network Analysis: Network analysis involves analyzing the social connections between users on a particular social media platform to identify patterns in how attitudes and identities are transmitted through social networks. This method can help identify influential users and communities that shape attitudes and identities on a particular platform. Network analysis can also provide insight into how users form and maintain social connections based on shared attitudes and identities.

Overall, analyzing the attitudes and identities of social media users requires a multidisciplinary approach that combines methods from computer science, psychology, sociology, and communication studies. By using a combination of methods, researchers can gain a more nuanced understanding of how social media users express their attitudes and identities, and how these expressions are shaped by social and cultural factors.