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Over the last two years, Matthew and I have been overhauling Mining the Social Web, preparing to release this technical manual in its third edition. I was brought on to help with the project, which ended up taking some interesting turns.
The project started (in a way) at PyCon 2016 in Portland, Oregon. It was late May and my first time in Oregon. I had flown down from Calgary, Alberta, where I was living at the time. A few months earlier I had defended a PhD in astrophysics and I was pretty burned out.
While there was still work remaining to get the manuscript of the thesis into its final form, I was ready to think other projects. My interests had begun to shift away from simulating star formation and towards machine learning. The term “data scientist” was still very new, but held great appeal to me. And having done mostly data analysis and writing for the last two years of my PhD, this seemed like a natural fit.
PyCon is this beautiful annual confluence of geeks who love Python. I felt at home. There that I met an editor from O’Reilly Media who thought a “data scientist” with my scientific background could be a good fit for this project. She invited me to follow up after the conference.
The danger with writing technical books is that technology moves faster than the publishing cycle. The proposed project was to join Matthew Russell in overhauling Mining the Social Web for the 3rd Edition. I was told this would mostly involve modernizing the code to Python 3, and testing everything to make sure the code still ran. That didn’t sound too difficult and I had done some data mining work with Twitter before, so I agreed.
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As you may recall, many things happened in 2016. Some of them involving social media.
Over the course of the last two years, in the wake of the Cambridge Analytica scandal and some major data breaches, social media has come under much more scrutiny and all the major platforms went on the defensive.
APIs were changed. Access to data was severely curtailed. Certain privileges required approval from the platform’s developers. Mining the Social Web was full of examples designed to teach data mining techniques and provide the reader with tools for building interesting applications. Suddenly a lot of the code no longer worked.
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.
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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. That’s a topic for a whole other book. Instead, we made use of some free Google Vision APIs and wrote code to have it “look” at Instagram photos and describe what they contained.
Goodbye Google+
As the finishing touches were being put on the book, another announcement was made that made all of us groan. Google was going to be sunsetting Google+. ip加速器破解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.
Hello MTSW3E
So while the publishing date was pushed back several times, we’re proud about how far the book has come. Mining the Social Web has undergone a thorough refresh and we plan to continue supporting the community through bug fixes and updates to the ip加速器破解.
Thank you to everyone who has waited so long for this project to finish. The book is available from Amazon, and digitally on the O’Reilly Safari Platform.
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Posted on February 14, 2014 Leave a Comment
Google has really been on the up-and-up lately with a service called Google Takeout that allows you to export your data from its cloud. For the thoughtful cloud user who is becoming increasingly concerned about privacy, accidental data loss, or data ownership, this is a product that’s sure to please. 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.
In a world filled with Twitter, Facebook, and other popular social networks, it’s easy enough to overlook mail data as mundane; however, your mailbox is without a doubt one of the places where you have probably accrued some of the most interesting data over the years. 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. Furthermore, each message consists of human language data that’s inherently expressive, and is laced with structured metadata fields that anchor the human language data in particular timespans and unambiguous identities.
…
Although social media sites are racking up petabytes of near-real-time social data, there is still the significant drawback that social networking data is centrally managed by a service provider that gets to create the rules about exactly how you can access it and what you can and can’t do with it. 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 6-3, 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.
The basic flow of the IP完美加速加速器 免费网络加速器 - 黑域基地-专注好用破解 ...:2021-8-24 · 发布一款之前没有发布过IP加速器免费版，常用一些网游都可伍完美加速，比较实用，另外不是说软件全是收费的。当你登录界面双击LOGO，可伍切换到其他几款加速器，那几款都是收费的，就靠那几款盈利，这个IP involves the following steps:
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- Convert the mbox export into JSON
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- Use MongoDB’s powerful aggregation framework to query and analyze the mailbox
As is the case with all other chapters from Mining the Social Web, all of the source code examples for Chapter 6 are available online in a convenient IPython Notebook format and easy enough to follow along with even if you don’t have a copy of the text. Furthermore, the turn-key virtual machine that’s provided takes care of the initial installation/configuration pains of IPython Notebook, MongoDB, and some of the other dependencies so that you can get right to the good stuff!
If you haven’t yet installed the virtual machine, this quick start guide that features a step-by-step video may be of great help, and as always, I’m just a tweet, Facebook message, GitHub ticket, or email away if you need any assistance along the way.
Enjoy.
Twitter Data Mining Round Up
Posted on February 10, 2014 Leave a Comment
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. This seemed like a natural starting point given that the first chapter of the book is a gentle introduction to data mining with Twitter’s API coupled with the inherent openness of accessing and analyzing Twitter data (in comparison to other data sources that are a little more restrictive.) Twitter’s IPO late last year also focused the spotlight a bit on Twitter, which provided some good opportunities to opine on Twitter’s underlying data model that can be interpreted as an interest graph.
Throughout the remainder of this coming year, I hope to spend much less time on Twitter and systematically work through the myriad other topics in the book: Facebook, LinkedIn, Google+, email, web pages, etc. However, before changing course, it seemed useful to provide a consolidated reference of the existing Twitter-related content.
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- Every MTSW blog post that’s tagged with “twitter” (and there are quite a few of them)
IPython Notebooks
- The full-text of Mining the Social Web 2E, Chapter 1 (pdf version; html version)
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- A primer on using pandas to understand the reaction to the Amazon Prime Air announcement with Twitter’s Streaming API (the “firehose”)
- An analysis of of some “celebrity” Twitter accounts (Tim O’Reilly, Lady Gaga, and Marissa Mayer)
Excerpts & Presentations
- Data Journalism and Interactivity (Quito, Ecuador; September, 2013)
- Why Twitter Is All the Rage: A Data Miner’s Perspective (Webinar; October 2013)
- Mining Social Web APIs with IPython Notebook (New York City; October 2013)
- Data Day Texas Presentation (Austin, TX; January 2014)
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- Why Is Twitter All the Rage? (book excerpt)
Videos
- Getting Started with Twitter’s API
- Why Twitter Is All the Rage: A Data Miner’s Perspective
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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.
Stay in touch and feel free to reach out with any suggestions or requests about future content.
Mining Social Web APIs with IPython Notebook [Data Day Texas Workshop Slides]
Posted on 伋理ip破解版无限试用 Leave a Comment
Thanks to everyone who attended the Mining Social Web APIs with IPython Notebook workshop at Data Day Texas. I’m really glad that I made the trip down to Austin and could share some of my work with you. The data truly is bigger in Texas, Austin was a fantastic city to visit, and everyone I had the pleasure of speaking with at the conference was really friendly and motivated to learn.
In case you missed the workshop, you can download the workshop slides from Slideshare. Everything you need to follow along should be in the deck, but as always, please don’t hesitate to contact me if there’s anything at all that I can do to help you in any way.
Gratefully,
-MAR
5 Questions for Aspiring Author-Entrepreneurs
Posted on December 31, 2013 1 Comment
For most of 2013, most of my nights and weekends have been consumed with a writing (and selling) a book entitled Mining the Social Web (2nd Edition). This makes the fifth tech book that I’ve written in approximately five years, and one thing I’ve come to learn over the course of my book writing adventures is that book writing is a skill in and of itself. Like anything else, the more of it that you do, the more that you learn and can share back with others.
This post presents the following questions (along with some anecdotal advice) that I’d recommend mulling over if you are an aspiring tech book writer.
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- How long will it take?
- To self-publish or not to self-publish?
- Is it a project or a product?
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Writing a quality tech book of reasonable length is not for the faint of heart. Like any other long-lived effort in an age of waning attention spans and instant gratification, some of the pains involved will push you to a point where you’ll seriously reconsider whether or not this book-writing idea was worthwhile in the first place. On more than one occasion, you’ll contemplate the other things that you could be doing with your time. In the end, if you don’t have a good reason as to why you’re writing the book, you’ll probably quit and be just another publishing casualty along the way.
To be perfectly clear, your motives certainly don’t have to be altruistic or selfless. 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:
- Rigorously learning a new topic
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- Earning extra income
- Altruistically fulfilling a need in the market
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If money is your primary interest in writing a tech book, you may find that you don’t have very many problems at all.
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I’d recommend thinking about the amount of effort that it takes to write a quality tech book in terms of both overall effort involved as well as calendar time. The former is based upon estimates that you’ll derive from your outline of the book and can be used to comparatively think about the “opportunity costs” of not doing something else with your time. The latter partitions that overall amount of time into a schedule that fits onto the calendar and helps you to better understand the ramifications of those opportunity costs.
Just a few of the opportunity costs that you should consider:
- Missed consulting revenue
- Volunteer work
- Exercise
- Social relationships
- Entertainment
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As with software projects, estimating the effort required to write a tech book can be quite difficult. Lots of early peer review on your outline and other efforts to ferret out the “unknown unknowns” that could adversely affect your project schedule is essential.
To illustrate, let’s assume that you’ve produced a solid outline that suggests you’ll be writing a book that’s estimated to be around 350 pages. Using a heuristic of 2 hours per page, that translates to about 700 hours of effort, and unless you’ve enjoyed a recent windfall or other special circumstances that allows you to approach this endeavor as a full-time job, you’ll be inevitably sacrificing a substantial portion of your nights and weekends for the better part of a year to get it done if you’re moonlighting at the rate of 15-20 hours a week.
One other consideration that you should always take into account with any activity involving estimation is Hofstadter’s Law, which is defined as follows: It’s always takes longer than Hofstadter’s Law predicts that it will, even when you take into account Hofstadter’s Law.
Seriously, estimation is not easy, and you’ll find that there are gaps in your outline that you’ll need to fill along the way. Those detours can really start to add up. The bottom line is that it will almost certainly take longer than you anticipate to write a book that you’ll be proud of writing. Be sure to regularly reassess your original estimates and update them along the way.
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Besides making that initial mental commitment to write a book, determining whether or not to work with a publisher and choosing a particular publisher is probably the biggest decision that you’ll make. I’d recommend approaching this very important decision with standard cost-benefit analysis as well as from the basis of whether or not you need a partner to achieve your goals for the book or if you can do it alone.
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However, you’re the one who will be staying up late and making lots of sacrifice to produce the book as a moonlighting activity, so you should be sure that the publisher can meet your own expectations before engaging in a (legally binding) partnership with them. A few questions to consider during your initial conversations with a publisher:
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- How much can I deviate from the original outline without renegotiating the contract?
- Will I ever be able to renegotiate any key financial metrics like royalty rates or advances?
- How much “production support” are you providing for professional illustrations, proofreading, copyediting, etc.?
- What will you do to market/sell the book once it’s complete?
There’s a real value that you can estimate and place on those factors. Sure, you could do it all yourself, but that would take up even more of your time and translate into even higher opportunity cost.
You could self-publish, but you could also do lots of other things with the time that it would take to produce a truly professional work. Carefully consider your motives and goals for producing the book before deciding that self-publishing is right for you.
In an era of self-publishing, ebooks, and print-on-demand services, I’d recommend that you hold the publisher to very high standards on at least the following fronts:
- The shaping and refinement of your initial ideas
- Don’t underestimate taking into account the importance of writing a book that the market needs as opposed to just writing a book that you want to write.
- Constructive criticism about your manuscript as it evolves
- You need the feedback, no matter how good you think that you are. You want your product to be the best that it possibly can be.
- The application of quality production processes to the final manuscript
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- A solid distribution channel with ample sales/marketing
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In my recent book-as-a-startup experiences with Mining the Social Web (2nd Edition), it’s the application of production processes and the distribution channel that have provided the most value. Multiple rounds of proofreading, copyediting, professional illustrations, and the creation of cover art are all things that I’d rather not have done for myself and certainly took the professionalism of the book to a whole new level. In terms of distribution, suffice it to say that it is certainly in the publisher’s interest to see your work succeed, but you are only one of scores of authors that they are probably working with, so temper your expectations.
One expectation that you should certainly not not misunderstand is that your publisher is not your primary source of sales and marketing. You as the author are your primary source of sales and marketing. Once you have a final product in a distribution channel, there will probably be some momentum from a small PR campaign around your book that the publisher takes care of, but that’s really just to set off a spark. The real sales and marketing is up to you, and you’ll have to be enterprising to figure out what’s working and what’s not working. I highly recommend the application of Lean Startup principles, which is a good segue into the next topic.
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- The process of writing a book is a project
- A book is a product that you sell
The takeaway here is that if you only think about your book as a project, then the project basically ends once you have a product in the publisher’s distribution channels. At that point, the project is “complete” aside from some ad-hoc work you might occasionally do to promote it. By the time the book publishes, you’re probably frazzled, exhausted, and just want to regain some balance in your life, so it’s a very natural reaction to feel a sense of accomplishment, breathe a sigh of relief, and trust that the publisher will sell it for you. After all, if it’s any good, it’ll just “sell itself”, right?
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I’m confident that you’ll make a few bucks with your book while you momentarily decompress from the surge to get it across the finish line, but I’d strongly admonish you to reengage and treat it like a product from that point forward. The decision to think of your book as a startup and yourself as the CEO of this tiny little startup is a lot more work compared to performing ad-hoc work whenever you feel like it, but it unlocks an entirely new perspective on life.
With a product and distribution channel in hand, you’ll be forced to think about things that you’ve always taken for granted (or thought of as unimportant/easy work) in other professional engagements. A few examples of the hats you’ll wear as an author-entrepreneur with your book-as-a-startup business to get you thinking:
- As CEO, what should you be doing to maximally promote the book? Blogging? Speaking engagements? Book tour? Should you spend money on various sources of online ads? Should the book just be a prop for consulting?
- As CMO, can you accurately estimate the size of your addressable market? Determine if your messaging is as effective as it needs to be?
- As COO, can you explain the prior month’s revenue? Forecast the next month’s revenue?
- As CTO, is there a way that you can simplify the user’s experience to try out the code? Perhaps a VM or a web app that’s trivial to install?
- As the SVP of Customer Service, can you institute a system to respond to unhappy readers? Before they leave you a bad review?
At the end of the month, it really all boils down a single number: revenue earned. The arithmetic and accounting reports (as provided by the publisher or online publishing system) are pretty simple. As the author-entrepreneur, it’s your job to do something about them.
What is holding you back from selling more books? Is it a flawed product, or is it a marketing issue?
Writing a book is one thing. Selling a book is a different beast entirely.
Marketing is hard.
The following video is a short ~5 minute Ignite talk that provides some (hopefully motivational and entertaining) information on the notion treating a book as a startup.
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Last but certainly not least is the longevity of your book, regardless of whether you prefer to think of it as a project or a product. In either case, you’ve invested non-trivial effort into making it a reality, and you probably won’t look forward to the maintenance involved in keeping it up to date, or the day that you have to rewrite significant portions to reflect changes in the underlying technology that backs the dialogue and example code.
As much as you need to understand your addressable market, you need to understand the technology that you are including in your book, the community that backs it, and any roadmaps that may (or may not) exist. Take it from someone who has written a book that was affected by fairly major changes to the social web landscape (short-notice Twitter API changes, the retirement of Google Buzz and the birthing of Google Plus, OAuth 2.0 evolution, etc.) that it’s not enough to just write about what exists right now.
You need to craft your written message so that it’s as evergreen as possible. In the words of a famous Canadian hockey player, you want to “skate where the puck’s going, not where it’s been”. Be as prescient as possible in making the right bets in terms of what you introduce in written form (the book) versus what you can provide as an online supplement that will be much easier to maintain. As with (successful) software projects, the majority of the effort required is usually during the maintenance of the product after it’s been operationalized. Why should a successful tech book be any different?
How does the shelf-life of your book compare to the shelf-life of these Twinkies?
Revenue is trust. If your customers trusted you enough to pay for a product with your name on the front of it, you can either take care of them and show yourself worthy of that trust, or you can inevitably tarnish your reputation. And that’s not good for business.
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Writing a successful tech book is an incredibly daunting endeavor, and if you really want to maximize the revenue opportunities associated with it, you’d be wise to think of it in terms of a tiny startup business, apply some Lean Startup principles, and treat yourself to the entrepreneurial education that only real world experience can bring. It will require more sacrifice than you think that it will, it will take more time than estimate that it will, things will go wrong, and the whole process will truly test you. However, you will come out the other side stronger, wiser, and with “street smarts” that you can’t get by just sitting around and talking about things.
Talk is cheap. Don’t be cheap. Get to work on that book, and let me know if there’s anything I can ever to do help you. I hope to share some more book-as-a-startup posts in early 2014.
Understanding the Reaction to Amazon Prime Air (Or: Tapping Twitter’s Firehose for Fun and Profit with pandas)
Posted on December 19, 2013 2 Comments
On Cyber Monday eve, Jeff Bezos appeared in a 60 Minutes segment and revealed to the world that he’s been working on an experimental effort called Amazon Prime Air. The general idea behind Amazon Prime Air is that Amazon may one day deliver relatively lightweight items directly to your doorstep in less than 30 minutes after you order via a fleet of small unmanned aerial vehicles. The following short video summarizes the concept in case you’ve somehow missed it.
Within moments of the announcement, I tapped Twitter’s firehose for the keyword query “Amazon” by employing a couple of recipes from the ip加速器破解, because this seemed like an ideal opportunity to capture a relatively large volume of tweets laden with emotional reaction. Over the course of the next few hours, I collected ~125,000 tweets, analyzed them in IPython Notebook with pandas, and later presented these findings as an online mini-workshop. (A video archive of the entire workshop is now available in case you missed it last week.)
Rather than rehashing the results here, I’d rather invite you to spend a few minutes reviewing the notebook. It’s easy to follow along with, features lots of narrative, and includes output from running the code. The analysis techniques range from basic times-series analysis with pandas to rudimentary natural language processing toward the end, so there should be a little something in there for everyone.
As always, questions and comments are welcome. Enjoy.
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Posted on November 23, 2013 Leave a Comment
A ~5 minute Ignite talk (20 slides, 15 seconds per slide) that provides some advice on writing tech books — and life.
The fundamental takeaway is that a book is a startup! (If you want it to be…)
- It’s a product (and/or services.)
- But it’s especially product
- Tech writing is a skill
- It’s story-telling
- Moonlighting is a skill
- Maintain work/life balance
- You can have a startup
- Write a book!
Download the slides on SlideShare.
伋理ip破解版无限试用
What Do Tim O’Reilly, Lady Gaga, and Marissa Mayer All Have In Common?
Posted on November 22, 2013 4 Comments
This post examines the followers of some popular Twitter users as the final installment of a multi-part series about exploring ip加速器破解 by asking the (Freakonomics-inspired) question, What do Tim O’Reilly, Lady Gaga, and Marissa Mayer all have in common? Although it may initially seem like an obnoxious question to ask, some of the answers may intrigue you once you begin to take a closer look at the data. (Although dashingly good looks might be one thing that they all have in common, we’ll let the data do the talking and stick with Twitter followers as the basis of computing similarity for this post.)
Which two of these three accomplished entrepreneurs are most alike? It all depends on the features that you’re comparing!
Goals
The initial idea behind this entire series on Twitter influence is that it would be an interesting and educational experiment in data science to put Tim O’Reilly‘s ~1.7 million followers under the microscope and explore the correlation between popularity (based upon number of followers) and Twitter influence.
In order to draw some meaningful comparisons, however, we’ll need to consider at least one other account. Marissa Mayer seems like a fine selection for comparison since her Twitter account is similar yet different to Tim’s account. For example, she’s also a “tech celebrity” and business executive. However, her particular expertise is not quite the same, and she only has about one-fourth as many followers. (Or so it would initially appear…)
Just to make this interesting, let’s further mix things up a bit by introducing a wildcard. Lady Gaga seems as good a choice as any to introduce a bit of unexpected fun into the situation. She is one of the ten most popular Twitter users based upon number of followers, an accomplished entrepreneur, and surely draws interest from a broad cross-section of the population. The introduction of a third account also provides the opportunity to draw some additional comparisons, so let’s compute the Jaccard index for the various combinations of these three accounts and see what turns up. The Jaccard index measures similarity between sample sets, and is defined as the size of the intersection divided by the size of the union of the sample sets, or, more plainly, the amount of overlap between the sets divided by the total size of the combined set. This is a simple way to measure and compare the overlap in followers.
Results
The full results (example code, notes, and the results from executing each cell) are available as an IPython Notebook, and you are encouraged to review it in depth. For convenience, a summary of the key results that you’ll see computed in the notebook follow:
- Approximately 50% of Tim O’Reilly’s ~1.7 million followers are “suspect” in the sense that they may be inactive accounts or spam bots. In comparison, only about 15% of Marissa Mayer’s ~460k followers are suspect according to the same criteria.
- Although mostly speculative, this difference might be explainable by a massive wave of spam-bots targeting popular users back in 2009 when Twitter experienced some unprecedented growth in its number of users. (For example, a closer look at the data reveals that ~66% of Tim O’Reilly’s followers joined Twitter in 2009.)
A histogram of Tim O’Reilly’s followers who have fewer than 10 followers of their own. Approximately 50% of these followers are “suspect” in that they may be spam-bots or inactive accounts; decreasing the threshold to 5 decreases the number to just under 40%.
- Approximately 25% of Tim O’Reilly’s (“non-suspect”) followers also follow Lady Gaga as compared to only about 18% for Marissa Mayer.
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- Lady Gaga has a higher Jaccard similarity to Tim O’Reilly than to Marissa Mayer. (However, Tim O’Reilly and Marissa Mayer have a much higher Jaccard similarity to one another than either one of them have to Lady Gaga, as might have been reasonably expected from their strong technology backgrounds.)
- Tim O’Reilly and Marissa Mayer have ~100k followers in common, and even once this number is adjusted for suspect followers, there are still ~95k followers in common. This is a high number but doesn’t seem all that surprising.
- What may seem a bit unexpected is that once you introduce Lady Gaga, this number only drops to ~25k. In other words, the total number of followers that Tim O’Reilly, Marissa Mayer, and Lady Gaga all have in common amongst the three of them is still about 25k accounts.
Perhaps the broad takeaway that addresses our initial inquiry about using popularity as an indicator of clout is that “number of followers” is not as clear cut a heuristic as it may have first seemed. After all, the actual gap between Tim O’Reilly and Marissa Mayer appears to be considerably smaller than it once appeared after making a simple adjustment for so-called “suspect” followers.
But what do Tim O’Reilly, Lady Gaga, and Marissa Mayer have in common? At least one way of answering the question is that there appears to be that there at least 25k common fans who are interested in all three of them. After all, Twitter is an interest graph. A closer analysis of these common account profiles could prove quite interesting and is a recommended exercise.
Although nothing definitive was proven, it seems quite likely that a coarse filter on an account’s followers is a good starting point. It wouldn’t be too difficult to perform some additional filtering to increase the precision of identifying abandoned accounts or spam bots that cannot be influenced in order to more accurately narrow in on a base metric for computing Twitter influence. You now have the tools and a good starting point to do just that — and a lot of other fun stuff.
By the way, you notice that we didn’t tell you how many of Lady Gaga’s followers appear to be spambots or inactive. That is the topic for another post to follow. (Unless, of course, you beat me to the punch!)
Enjoy!
Updates
23 Nov 13 @ 1900UTC – Like Tim O’Reilly, approximately 50% of Lady Gaga’s followers are also “suspect” when applying the same “minimum follower” filter. She joined Twitter around the same time as Tim O’Reilly back in March 2008.
More analysis to follow soon with a closer look at ‘suspect’ followers with the goal of identifying the inactive/spambot accounts with very high probability. Thoughts on criteria to use are welcome. Leave a comment
Resources
- An HTML export of the IPython Notebook for this post
- The collection of IPython Notebooks containing the source notebook for this post
- Mining the Social Web on GitHub
- Screencasts that show you how to install the social web mining toolkit as a virtual machine
- Previous posts in this series on Twitter influence
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Posted on ip加速器破解 3 Comments
In the last few posts for this series on computing twitter influence, we’ve reviewed some of the considerations in calculating a base metric for influence and how to acquire the necessary data to begin analysis. This post finishes up all of the prerequisite machinery before the real data science fun begins by introducing MongoDB as a staple in your social web mining toolkit and showing how to employ it for storing social data such as Twitter API responses.
As Easy As It Should Be
MongoDB is an excellent option to consider if you need a quick and easy fix for your data science experiments, and if you like Python, there’s a good chance you’ll enjoy MongoDB as well. Much like Python, MongoDB easy to pick up along the way, it scales up fairly well as the size of your data grows without too much fuss, the online documentation is excellent, the community is robust, language bindings are plentiful, and it’s generally just as easy as it should be to do a lot of data manipulation to/from Python.
MongoDB is an excellent option to consider if you need a quick and easy fix for your data science experiments…
MongoDB document-oriented, which (for our purposes) basically means that it stores JSON data, enabling you to easily archive the responses that you get back from most social web APIs. It’s easy enough to query the data with the standard find() operator, but a more powerful aggregation framework is available for constructing more nuanced data pipelines.
A primer of MongoDB is unwarranted, but if you have a copy of the book on hand, Chapter 6 (Mining Mailboxes) introduces a MongoDB as a sort of surrogate API for mail data. (The first half of this chapter focuses on normalizing arbitrarily sourced mail data so that it can be ingested into MongoDB for standardized analysis.)
【全球伋理加速器】-百度搜索详情 - SEO追词网:全球伋理加速器近30日平均搜索极少次，其中移动端极少次，pc端极少次；目前竞价非常激烈，慎重考虑，在过去的一周内，全球伋理加速器在精确触发下推至页首所需要的最低价格为0.91元。百度收录与全球伋理加速器有关结果141,000个。前50名中有20个顶级域名，1个二级域名，3个目录，26个文件。 (Example 9-7 from the Twitter Cookbook) introduces two functions for storing and retrieving Twitter API data from MongoDB that we’ll adapt in the next section for our immediate needs. Take a moment to review this recipe if you haven’t previously encountered it. The functions that it provides are little more than load/store convenience wrappers.
Storing Millions of Twitter Followers
Recall from the last post in this series that a recipe like Getting all friends or followers for a user (Example 9-19 from the Twitter Cookbook) is fundamentally limited by the amount of memory that’s available. It buffers API responses in memory and accumulates 75,000 long integer values every 15 minutes, and although this is fine for a user with a “reasonable” number of followers, it won’t work at all for celebrity users with millions of followers. Even if we did have unlimited heap space, we’d still want to strive for a low memory profile as well as maintain a persistent archive for more convenient analysis that’s unconstrained by rate limits and network latency. After all, once you have the data, you won’t want to go to the trouble of fetching it again unless absolutely necessary since this process can be quite time consuming.
To illustrate just how easy it is to adapt a recipe from the cookbook like Example 9-19, take a look at this revised version of get_friends_followers_ids that’s been renamed to store_friends_followers_ids and compare it back to the original version. The primary substance of the change is simply the introduction of a save_to_mongo call for persisting each API response (along with a few tweaks to make this possible.)
def store_friends_followers_ids(twitter_api, screen_name=None, user_id=None,
friends_limit=maxint, followers_limit=maxint, database=None):
# Must have either screen_name or user_id (logical xor)
assert (screen_name != None) != (user_id != None), "Must have screen_name or user_id, but not both"
# See http://dev.twitter.com/docs/api/1.1/get/friends/ids and
# See http://dev.twitter.com/docs/api/1.1/get/followers/ids for details on API parameters
get_friends_ids = partial(make_twitter_request, twitter_api.friends.ids, count=5000)
get_followers_ids = partial(make_twitter_request, twitter_api.followers.ids, count=5000)
for twitter_api_func, limit, label in [
[get_friends_ids, friends_limit, "friends"],
[get_followers_ids, followers_limit, "followers"]
]:
if limit == 0: continue
total_ids = 0
cursor = -1
while cursor != 0:
# Use make_twitter_request via the partially bound callable...
if screen_name:
response = twitter_api_func(screen_name=screen_name, cursor=cursor)
else: # user_id
response = twitter_api_func(user_id=user_id, cursor=cursor)
if response is not None:
ids = response['ids']
total_ids += len(ids)
save_to_mongo({"ids" : [_id for _id in ids ]}, database, label + "_ids")
cursor = response['next_cursor']
print >> sys.stderr, 'Fetched {0} total {1} ids for {2}'.format(total_ids, label, (user_id or screen_name))
sys.stderr.flush()
# Consider storing the ids to disk during each iteration to provide an
# an additional layer of protection from exceptional circumstances
if len(ids) >= limit or response is None:
break
print >> sys.stderr, 'Last cursor', cursor
print >> sts.stderr, 'Last response', response
# Sample usage follows...
screen_names = ['SocialWebMining', 'LadyGaga']
twitter_api = oauth_login()
for screen_name in screen_names:
store_friends_followers_ids(twitter_api, screen_name=screen_name,
friends_limit=0, database=screen_name)
print "Done"
That’s really all that there is to it. We’re now to the point that we can reliably harvest and store arbitrary volumes of Twitter data.
It may be worthwhile to review the prior posts in this series as a reminder for just how far we’ve come so far. Now having all of the necessary machinery and prerequisite discussion in place, we’ll return to the original proposition of computing Twitter influence with an initial review of some data for a few well-known Twitter accounts in the next post in this series.
ip加速器破解
- Previous posts in this series about computing Twitter influence
- ip加速器破解
- MongoDB documentation
- Mining the Social Web‘s ip加速器破解
- ip加速器破解 for installing a virtual machine with MongoDB and other social web mining tools
How to Deliver a Successful Tech Workshop with Vagrant and AWS
Posted on 伋理ip破解版无限试用 1 Comment
At Strata, I delivered workshop called 电脑加速器永久免费版哪里可伍下？有没有免费的加速器 ...:2021-9-8 · 电脑加速器永久免费版哪里可伍下？pdf电脑加速器哪个好？ 【易通IP加速器】易通vpn做好IP互转的功能，使用易通网络加速器可伍突破防火墙限制达到你想跟换的IP地址，拥有国内各省市线路多IP地址5000条IP地址，国外7000条IP地址，强大的各地区 ..., and in order to ensure that the workshop would meet its objectives and be a smashing success, I knew that a few constraints had to be considered:
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- You need a development environment to follow along with the examples. (You can’t do anything with data unless you have a development environment.)
- Most people wouldn’t have prepared a development environment. (Inevitable.)
- Preparing a development environment is isn’t possible to do on site at the workshop. (It’s far too time-consuming and the wireless would probably buckle even if it weren’t.)
Those constraints are actually pretty challenging to satisfy, but there are some approaches that you can consider:
- Do nothing; if people didn’t prepare, then it’s too bad for them. (Unacceptable if you want people to enjoy your workshop. Even if it’s not your fault that they didn’t prepare, it’s still your fault that they didn’t prepare.)
- Pass out media such as CDs or USB drives with the necessary software on it on site. (Cumbersome at the very least for a non-trivial number of attendees and still could be fairly time consuming.)
- Provide pre-configured cloud-based machines for everyone. (Check.)
Running a Vagrant Box on AWS
Powering Mining the Social Web’s virtual machine experience with Vagrant has turned out to be a remarkably good decision. It trivializes the process of bootstrapping a virtual machine and applying a configuration management template, which is perfect for creating a repeatable development environment. Just follow along with the quick start guide, watch the screencasts, and you’ll be up and running in no time.
But that’s the Vagrant you know and not the Vagrant that would do much to help with the workshop.
The Vagrant that you (probably) don’t know is the Vagrant that can just as trivially launch that very same virtual machine on the AWS cloud. In short, you just need an AWS account and the vagrant-aws plugin. In a little more detail, here are the basic steps involved once you’ve already been able to follow along with the quick start guide and launch your virtual machine locally:
- 伋理ip破解版无限试用 for an AWS account. (Right now, there’s even a “free tier” that will work just fine for Mining the Social Web, so it won’t even cost you anything. However, you are required to have a credit card on file.)
- Install the vagrant-aws plugin. Type this in a terminal: 【tplink路由器密码破解工具下载】tplink路由器密码破解工具 ...:2021-9-19 · tplink路由器密码破解工具是一款专门针对tplink路由器的密码破解工具，它采用多线程破解算法，能够破解的几率很大，同时可伍显示密码，助力大家成功进入路由器配置界面，还可伍找回忘记的密码。还在为蹭网而心烦吗，
- Install a “dummy” Vagrant box, which just creates a shell for Vagrant to use for some local bookkeeping. Type this in a terminal: vagrant box add dummy http://github.com/mitchellh/vagrant-aws/raw/master/dummy.box
- Define the four environment variables starting with 伋理ip破解版无限试用 that are referenced in Mining the Social Web’s Vagrantfile. These environment variables define your AWS access key, AWS secret access key, the name of the keypair used to start an EC2 instance, and the path to the private key for that keypair.
- Start your virtual machine in the cloud. Type vagrant up –provider=aws and keep an eye on the console
As with anything else, there may be a few configuration details that you’ll need to tweak, but that’s the gist. If you’re interested in launching a virtual machine with AWS provider, you should definitely learn some EC2 fundamentals, and read up enough about Vagrant to understand the contents of the Vagrantfile. In particular, bone up on the details associated with launching EC2 instances (so that you have a better idea of some of the other settings you can configure such as the region in which your AWS machines will start and those sorts of things), and invest a little bit of time learning more about Vagrantfile settings.
Running a Vagrant Box on AWS
The instructions in the previous section are exactly what you’d do to bootstrap a single AWS instance in the cloud, and unless you’re doing some fairly heavy duty work, you should be able to employ a micro-instance that costs less than $0.02 per hour! However, recall that for my workshop, I didn’t need just a single instance. I needed to launch ~60 machines so that everyone in the workshop would have their own virtual machine.
As it turns out, it’s not so difficult to go from one to sixty. Here’s how:
- Login to your AWS management console to view running EC2 instances. (You’ll see the instance that Vagrant started on your behalf.)
- Create an AMI from your running EC2 instance. (An AMI is an “Amazon Machine Image”; think of it as a template for an EC2 instance.)
- Launch new EC2 instances from the “AMIs” item in the navigation menu.
Once Vagrant has configured your EC2 instance, you can create an AMI from the running instance. Then, you only need access to the AWS management in order to launch your instance from the AMI. Launching from the AMI usually takes less than 1 minute since all of the configuration management has already been applied.
Again, you will need to gain a little comfort with AWS along the way, but that’s pretty much all that’s required for a basic setup. At this point, you technically don’t even need Vagrant anymore since you can launch fully pre-configured EC2 instances as needed. (However, do keep in mind how much work Vagrant did to make it this easy to create the AMI image that you can now so easily employ.)
One other consideration worth pointing out is that you may want to think about securing your IPython Notebook server with a password since it’s in the cloud and could be accessible to anyone in the world if you haven’t locked down the range of IP addresses that access it. (Even then, a password would still probably be a good idea.)
Finally, note that your account will only be able to launch 20 EC2 instances by default, but that AWS customer service is ready and willing to help you if you need more. (Read more about my terrific encounter with AWS customer support.)
Now, go out and deliver a successful technical workshop!
Additional Resources
If you’ve found this post interesting, you may also enjoy these other resources:
- Mining the Social Web’s Quick Start Guide
- Screencasts about social web mining
- Mining the Social Web with IPython Notebook (Workshop slides)
