Twitter

Explore X (formerly Twitter) data clusters to uncover user behaviors (e.g., repost and reply patterns) within online communities. This guide focuses on a politically charged data set to illustrate the process of visualizing and analyzing social data.

Centrality and text analysis allow users to get more out of their social network data. Here’s how you can leverage them using R and Gephi.

Thanks to rapid advances in technology, large amounts of data generated on social networks can be analyzed with relative ease, especially for those who use the R programming language and Gephi.

Twitter is a goldmine of data. Unlike other social platforms, almost every user’s tweets are completely public and pullable.

In this tutorial, Toptal Freelance Software Engineer Anthony Sistilli will be exploring how you can use Python, the Twitter API, and data mining techniques to gather useful data.

Social networks are among the biggest sources of data today, and this means they are an extremely valuable asset for marketers, big data specialists, and even individual users like journalists and other professionals. Harnessing the potential of real-time Twitter data is also useful in many time-sensitive business processes.

In this article, Toptal Freelance Software Engineer Hanee’ Medhat explains how you can build a simple Python application to leverage the power of Apache Spark, and then use it to read and process tweets to identify trending hashtags.

Analysts have come to recognize social network data as a virtual treasure trove of information for sensing public opinion trends and groundswells of support. In this article, Toptal Engineer Elder Santos describes the techniques he employed for a proof-of-concept that effectively analyzed Twitter Trend Topics to predict, as a sample test case, regional voting patterns in the 2014 Brazilian presidential election.