This Python source code for Twitter API is based on "Mining the Social Web (Second Edition) and provides sample code for accessing Twitter's API for development and oAuth access for production purposes, discovering trending topics, searching for tweets, constructing convenient function calls, saving and restoring JSON data with flat-text files, using MongoDB, sampling the Twitter firehose with the Streaming API, collecting time-series data, extracting tweet entities, finding the most popular tweet and tweets in a collection of tweets, tabulating frequency analysis, finding users who have retweeted a status, extracting a retweet attribution, making robust Twitter requests, resolving user profile information, extracting tweet entities from arbitrary text, getting friends and followers for a user, analyzing user's friends and followers, harvesting a user's tweet, crawling a friendship graph, analyzing tweet content, summarizing link targets, and analyzing a user's favorite tweet.
Less than a month ago, Google made all public activities available via its "firehose." Last week, the search giant updated the Buzz API again with several interesting features, including a limited firehose, which is calls a "garden hose."
The existence of APIs has made it possible for organizations to collect and aggregate unprecedented amounts of customer data. What’s not so clear is how to actually go about harnessing it. That issue is giving rise to a host of customer intelligence applications that leverage APIs to ingest data and then, in turn, make analysis of that data available via an external API.
PeopleBrowsr is another “noise to signal” processor of the social media tidal wave. The Research.ly platform they provide is impressive and engaging. I had to pry myself away from it. It is a tool to answer the question, "who in this social media landscape is relevant on this topic?" but that is just the beginning. The PeopleBrowsr API is a pay to play way to gain social media intelligence.