By Matthew A. Russell

Fb, Twitter, and LinkedIn generate an enormous volume of precious social information, yet how are you going to discover who is making connections with social media, what they’re conversing approximately, or the place they’re situated? This concise and sensible publication indicates you the way to reply to those questions and extra. you are going to the best way to mix social net facts, research recommendations, and visualization that will help you locate what you have been trying to find within the social haystack, in addition to invaluable details you did not be aware of existed. every one standalone bankruptcy introduces recommendations for mining facts in several parts of the social net, together with blogs and e-mail. All you must start is a programming heritage and a willingness to profit uncomplicated Python instruments.

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Extra resources for Mining the Social Web: Analyzing Data from Facebook, Twitter, LinkedIn, and Other Social Media Sites

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If source not in ("RT", "via") ] ... >>> for tweet in all_tweets: ... rt_sources = get_rt_sources(tweet["text"]) ... if not rt_sources: continue ... for rt_source in rt_sources: ... add_edge(rt_source, tweet["from_user"], {"tweet_id" : tweet["id"]}) ... info Figure 1-1. A distribution illustrating the degree of each node in the graph, which reveals insight into the graph’s connectedness The built-in operations that NetworkX provides are a useful starting point to make sense of the data, but it’s important to keep in mind that we’re only looking at a very small slice of the overall conversation happening on Twitter about SNL—500 tweets out of potentially tens of thousands (or more).

Fortunately, others have also recognized these kinds of problems and decided to do something about them. , but it also recognizes that various social networking sites might expose different URLs that all link back to the same person. user=matthew might resolve to the same person for a given social networking site. Geocoordinates: A Common Thread for Just About Anything Omitting a discussion of microformats like geo and hRecipe as not being particularly useful for mining the social web would be a big mistake.

Geocoordinates: A Common Thread for Just About Anything Omitting a discussion of microformats like geo and hRecipe as not being particularly useful for mining the social web would be a big mistake. Although it’s certainly true that standalone geo data in no particular context isn’t necessarily social, important but much less than obvious relationships often emerge from disparate data sets that are tied together with a common geographic context. Geo data is ubiquitous and plays a powerful part in too many social mashups to even name, because a particular point in space can be used as the glue to cluster people together.

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