Mining and Analyzing Twitter trends: Frequency based ranking of descriptive Tweets

TL;DR

This paper proposes a frequency-based ranking method for filtering and identifying the most descriptive and relevant tweets about trending topics on Twitter, enhancing trend analysis accuracy.

Contribution

It introduces a novel frequency-based approach to rank tweets by relevance, improving the extraction of descriptive content in trend analysis.

Findings

Effective identification of descriptive tweets using frequency analysis

Improved relevance ranking of tweets based on word and hashtag frequencies

Potential for better trend communication and summarization

Abstract

One of the major sources of trending news, events and opinion in the current age is micro blogging. Twitter, being one of them, is extensively used to mine data about public responses and event updates. This paper intends to propose methods to filter tweets to obtain the most accurately descriptive tweets, which communicates the content of the trend. It also potentially ranks the tweets according to relevance. The principle behind the ranking mechanism would be the assumed tendencies in the natural language used by the users. The mapping frequencies of occurrence of words and related hash tags is used to create a weighted score for each tweet in the sample space obtained from twitter on a particular trend.