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Trend analysis in social media tracks the popularity and changes in discussions over time. For instance, hashtags related to an event can be monitored to see how the conversation evolves.

Text classification applies NLP to categorize social media posts. For example, support requests on social platforms can be classified for routing to the correct department.

Bot detection aims to identify non-human automated accounts that might skew social media analysis results. For example, distinguishing genuine user interactions from bot-generated content is essential for accurate data.

Keyword tracking monitors the frequency and context of keywords to measure interest and discussions around specific topics. Marketers might use this to monitor brand mentions or campaign performance.

Hashtag analysis involves examining the use and spread of hashtags to gain insights into content virality and user engagement. For example, evaluating the reach of a promotional hashtag during a marketing campaign.

User demographics analysis leverages NLP and sociolinguistic markers to infer characteristics of social media users such as age, gender, or location, and personalize content or advertising to these demographics.

Content personalization employs NLP to tailor social media content to individual users' preferences. For example, a user's interaction with certain posts can influence the personalized feed algorithm.

Influence measurement quantifies the impact or reach of users or content on social media. Tools like Klout used to provide scores representing a user's online influence based on their social media activities.

Sentiment analysis, or opinion mining, involves the use of NLP to determine the attitude or sentiment of social media users. For example, companies may analyze tweets mentioning their brand to gauge public sentiment.

Social network analysis examines the relationships between social media users. For example, it can identify influencers by analyzing who is most connected or mentioned within a network.

Image and video analysis uses computer vision techniques alongside NLP to understand the content and context of visual media shared on social platforms. This adds depth to social media analysis by including multimedia content.

Chatbots use NLP to simulate conversation with users on social media. They can help in customer service by responding to queries posted on a company's social media page.

Emoji analysis uses NLP to understand the contextual usage of emojis in social media interactions. This can paint a clearer picture of users' sentiments and attitudes in a non-verbal manner.

Topic modeling identifies themes or topics within large text corpora by clustering groups of similar words. A common algorithm for this is Latent Dirichlet Allocation (LDA). Analyzing Twitter posts could reveal trending topics.