By Matthew A. Russell
Millions of public Twitter streams harbor a wealth of information, and when you mine them, you could achieve a few important insights. This brief and concise ebook deals a suite of recipes that will help you extract nuggets of Twitter details utilizing easy-to-learn Python instruments. each one recipe bargains a dialogue of ways and why the answer works, so that you can quick adapt it to suit your specific wishes. The recipes contain recommendations to:
* Use OAuth to entry Twitter information
* Create and learn graphs of retweet relationships
* Use the streaming API to reap tweets in realtime
* Harvest and research acquaintances and fans
* notice friendship cliques
* Summarize webpages from brief URLs
This ebook is an ideal spouse to O’Reilly's Mining the Social Web.
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Stderr, 'Fetched %i total ids for %s' % (len(ids), user_id) # Consider storing the ids to disk during each iteration to provide an # an additional layer of protection from exceptional circumstances. sadd(rid, _id) for _id in next_queue ] d = 1 while d < depth: d += 1 (queue, next_queue) = (next_queue, ) for _fid in queue: _follower_ids = get_all_followers_ids(user_id=_fid, limit=limit) # Store a fid => _follower_ids mapping in Redis or other # database of choice. 16 Crawling Followers to Approximate Potential Influence | 47 # The total number of nodes visited represents one measure of potential influence.
As a more technical aside, be advised that finding cliques is a hard problem in both a figurative sense, but also in the computational sense that it’s a problem that belongs to a class of problems that are known as NP-Complete. In layman’s terms, being NPComplete means that the combinatorics of the problem, whether it be finding cliques or otherwise, grow explosively as the size of the input for the problem grows. Practically speaking, this means that for a very large graph, you either need to wait a very long time to get an exact answer to the problem of finding cliques, or you need to be willing to settle for an approximate solution.
Redis is trivial to get up and running, written in extremely lean C code, and can perform operations in memory and store the result as another key-value pair. In the case of sets, that means that you can perform intersections and differences directly in Redis and fetch the result in a manner that is dramatically more efficient than fetching data, performing the computation in your own application logic, and then sending data back to whatever you retrieved it for storage. io/download, and for Unix users, amounts to little more than a make command followed by invoking the redis-server binary.
21 Recipes for Mining Twitter by Matthew A. Russell