As an author, I also had to consider some moral questions around data mining. Was it ethical to be teaching others how to programmatically pull and sift data from Facebook, Instagram, Twitter, and elsewhere? As I wrote in the Preface to the 3rd Edition, there are many positive uses for data mining, even when the data comes from social media.
There are many examples of data mining and data analysis being used for social good see, for example, the DSSG Fellowship. I also wanted people to understand just how much metadata is attached to the things they post online, especially on public platforms like Twitter and Instagram. This metadata is mostly invisible to the user logged into these apps, but accessible over the API. And so over the course of many months, I wrote new code examples, rewrote some of the old ones, updated the API calls, updated the manuscript, and modernized the Python code. Then Matthew and I realized that the book really needed a chapter on Instagram.
Since the 2nd Edition, Instagram had exploded in popularity. There are currently about 1 billion monthly active users on the platform and the book did not have a chapter on it. This needed to change. Instagram is different from the other platforms we covered because Instagram is a visual platform. Mining text or metadata is one thing, but analyzing images requires computer vision. I introduced basic artificial neural networks in the chapter, but we were not about to roll our own deep convolutional network and train it on ImageNet. As the finishing touches were being put on the book, another announcement was made that made all of us groan.
Mining the Social Web was going to look immediately dated if we had an entire chapter devoted to a social network that was about to disappear. So Matthew heroically rewrote the chapter, keeping many of the great examples around mining text data, which are universal, and making sure that our book would have a better shelf life. Mining the Social Web has undergone a thorough refresh and we plan to continue supporting the community through bug fixes and updates to the GitHub repository. Thank you to everyone who has waited so long for this project to finish. Likewise, for the data mining enthusiast, quantified-self number cruncher, or hacker looking for a fun weekend project, Google Takeout is also a great option that enables some good fun.
The opening paragraph of Chapter 6 from Mining the Social Web, 2nd Edition is quick to highlight the interestingness of mailbox data and some of the possibilities:. Mail archives are arguably the ultimate kind of social web data and the basis of the earliest online social networks.
Mail data is ubiquitous, and each message is inherently social, involving conversations and interactions among two or more people. Mail archives, on the other hand, are decentralized and scattered across the Web in the form of rich mailing list discussions about a litany of topics, as well as the many thousands of messages that people have tucked away in their own accounts. When you take a moment to think about it, it seems as though being able to effectively mine mail archives could be one of the most essential capabilities in your data mining toolbox.
The remainder of Chapter 6 goes on to provide a fairly standalone soup-to-nuts primer on the nature of mail data, how to munge it into a convenient mbox format regardless of its original source , and how to use a document-oriented database like MongoDB to facilitate running analytics and extracting some meaningful insights. The text itself leverages the well-known public Enron corpus as a realistic source of open data, but the code works just as well with any other kind of mail data that can be exported or munged into an mbox format.
As it turns out, Google Takeout can export your entire mailbox or any subset of it as defined by labels and other organizational options you can implement through the standard GMail user interface, and after a couple of relatively minor enhancements , it became easy enough to forget all about Enron, pick up right at Example , and work through the remainder of the chapter on your own mailbox data. Likewise, many popular mail clients allow you to export in mbox format and accomplish the very same thing.
Since the release of Mining the Social Web, 2E in late October of last year, I have mostly focused on creating supplemental content that focused on Twitter data.
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However, before changing course, it seemed useful to provide a consolidated reference of the existing Twitter-related content. While there are plenty of other great links out there on the web about data mining with Twitter, these are a few that I am particularly proud to have produced. I hope you enjoy them. In case you missed the workshop, you can download the workshop slides from Slideshare. For most of , most of my nights and weekends have been consumed with a writing and selling a book entitled Mining the Social Web 2nd Edition.
Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Google+, GitHub, and More
Like anything else, the more of it that you do, the more that you learn and can share back with others. Writing a quality tech book of reasonable length is not for the faint of heart. You just need to be honest with yourself, clearly articulate them in writing somewhere, and review them from time to time. A few of the possible reasons you might consider writing a tech book could include:. The difference between doing something for fun versus doing it for profit can dramatically change the dynamics and relative enjoyment of the activity.
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Spara som favorit. Skickas inom vardagar. Mine the rich data tucked away in popular social websites such as Twitter, Facebook, LinkedIn, and Instagram. With the third edition of this popular guide, data scientists, analysts, and programmers will learn how to glean insights from social media-including who's connecting with whom, what they're talking about, and where they're located-using Python code examples, Jupyter notebooks, or Docker containers.
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