Saturday, December 31, 2016

Best Internet Politics Quick-Reads of 2016...

Make yourself more interesting at your New Year's Eve party with these...


Tuesday, November 08, 2016

Election Day Data: Twitter Sentiment

Academics and researchers will be looking this Election Day for any data that might indicate to what extent social media can help predict electoral outcomes. Towards this end, I've created my own software that analyzes Twitter sentiment for each presidential candidate.

Here's how it works. Written in Python, it uses the Twitter Streaming API to collect thousands of live tweets per minute about either Hillary Clinton or Donald Trump. The software then pipes all of those tweets through IBM's Artificial Intelligence engine (the Alchemy API) to determine, for each tweet, if the sentiment is Positive, Negative, or Neutral about that candidate.

Take this data only for what it is and avoid reading too much into it. For instance, it may say more about Twitter users than about the political candidates; it also may say more about the quality of IBM's language analysis algorithms than about actual sentiment; and more weaknesses abound.

That said, here's the data...

Of people tweeting about Hillary Clinton on this morning of Election Day, the sentiment is:

  • 19.5% Positive
  • 42.3% Negative
  • 38.3% Neutral
 Notably, that is a wider spread between her positives and negatives than she's had in the past (and not a promising one).


Election Day Data: The Betting Markets

To save some data for posterity, here is what the political betting markets have had to say about today's presidential election.

As of this morning (Election Day)...

(if you're wondering why these numbers add up to more than 100, remember these are betting markets, not percentages.)

For comparison, public polling currently shows Clinton leading Trump by only a 45.5% - 42.2% margin.

Also, here is a a chart illustrating how the betting markets have fluctuated for the two candidates over the past 90 days leading up to the election...

More to come.


Wednesday, September 21, 2016

What Ted Cruz Doesn't Understand About 'Controlling the Internet'...

Senator Ted Cruz is once again threatening a shutdown of the United States government - this time for the reason that the U.S. shouldn't "give foreign governments greater control over the Internet".

What is he talking about?

On October 1st, the Internet Corporation for Assigned Names and Numbers (ICANN), is scheduled to be turned over to an international consortium group for management. Cruz has made bold statements about how this is a national security issue that will lead to widespread censorship and enable countries like Russia and China to make decisions about what can and cannot be posted online.

This is completely ridiculous, and here's why. ICANN doesn't manage the Internet. All ICANN does is manage the Internet's domain name system. When you want to buy a domain name like "", they simply keep track of the fact that now you are the owner of that domain name. They have no effect whatsoever on how the Internet functions beyond maintaining a list linking IP addresses to domain names.

Cruz has Internet experts and computer scientists scratching their heads in bewilderment as to how little he understands the technical dimensions of this issue. Even Tim Berners-Lee, the creator of the World Wide Web itself, wrote an op-ed today stating, in no uncertain terms, just how wrong he is.

Here's the bottom line - ICANN and the domain name system play no role in Internet censorship. When the Internet is censored, it is accomplished by national firewalls (See Russia, China, etc.), through ISPs, and, as Cory Doctorow points out, massive armies of trolls for hire and other astroturfers, as well as copyright abuse.

So where is Cruz even getting these ideas? My guess is that he's reading headlines about how "Internet Governance" is about to shift out of U.S. control. What he doesn't realize is that the phrase "Internet Governance" has a special meaning in academia, referring solely to governance of the domain name system. It appears nobody informed him of this, and he made a common assumption based on the phrasing, which happens to be completely wrong.


Tuesday, September 20, 2016

Do Algorithms Have a 'White Guy' Problem?

Algorithms are coming to dominate many aspects of our lives and most people are completely unaware of it. They are being used to determine which job applicants actually get called for an interview, which people are awarded a mortgage or business loan, which stores will open in a community, which news headlines and opinions we're likely to see on social media, and much more.

The question that has made headlines recently is whether these algorithms are biased and discriminatory. Some like ProPublica's Julie Angwin and Microsoft's Kate Crawford have argued that algorithms and Artificial Intelligence have a "white guy problem" - meaning that, since algorithms learn by being fed certain data, and they build models of the world based on that data, the people doing the feeding are tremendously important because as they make selections informed by their own biases, the resulting software comes to incorporate those same biases.

They cite these recent examples:
  • "Predictive policing" in places like New York is used to forecast where crime is most likely to occur and directs police to focus on  those neighborhoods. Crawford states that, 
At the very least, this software risks perpetuating an already vicious cycle, in which the police increase their presence in the same places they are already policing (or overpolicing), thus ensuring that more arrests come from those areas. In the United States, this could result in more surveillance in traditionally poorer, nonwhite neighborhoods, while wealthy, whiter neighborhoods are scrutinized even less. Predictive programs are only as good as the data they are trained on, and that data has a complex history. 

From my own anecdotal perspective, I remember once when my wife and I were sitting next to each other on the couch - both using Facebook on our laptops - and she was shown ads for diaper coupons while I was shown ads for getting a second job; nevermind that I'm the stay-at-home parent and she's the one who works full-time. How's that for gender stereotypes being ingrained in an algorithm?

While these biases clearly exist, it's more difficult to prescribe realistic remedies. Some ideas thrown out there include making the algorithms more transparent and accountable - the idea of "algorithmic auditing". Also, since algorithms reflect the values of its creators, there's the idea that we must address not only the people who design them, but also the people who sit on the company boards, and which ethical perspectives are to be included.

It seems to me that the real danger isn't that algorithms are being used in the areas that they are, but that they are being used in ways that overlook the merits of the individual in favor of larger group associations. For instance, credit scores (long determined by algorithms) are based on whether each individual pays their bills on time, their individual history, their individual access to more credit as needed, etc. While still flawed, at least these credit scores are based on what you do as a person, so you have some level of control over your score.

By contrast, these other algorithms in the news recently are more problematic because a person's "scores" are completely dependent on what neighborhood you live in and what racial/ethnic/religious group you were born into. In other words, unlike credit scores, these algorithms are explicitly designed and programmed to make judgments about you based on group stereotypes rather than on the actions you take and decisions you make as an individual.

All of us would like to be defined by what we do as individuals, not by what other people "similarly situated" to us do. There should be universal condemnation of a system in which an upstanding and responsible person can't get a mortgage or business loan solely because their neighbor defaulted on theirs.

The good news is that since algorithms are programmed by people, people can program them differently. If values like meritocracy and individualism are important then they can become the constitutionally defining principles of algorithms going forward. Right now, though, they are a problem, reinforcing the worst stereotypes and acting as an obstacle to individual advancement. As Alistair Croll has famously said, "this is our generation's civil right's issue, and we don't know it".


Sunday, July 31, 2016

Do the Political Party Conventions Matter? Not according to the Betting Markets...

Think what you will about the party conventions that just wrapped up, but one thing worth pointing out is that, despite the so-called boost in polling that candidates receive coming out of their convention, temporary as it may be, this boost doesn't really exist if you look at a different metric than polls - the political betting markets.

Political betting markets have often proved to be a better predictor of electoral outcomes than public opinion polls, particularly the further away the analysis is done from Election Day.  So with the Republican and now Democratic conventions just completed, this is what the betting markets have had to say regarding who will win the presidential election...

Click image to enlarge

This is a 30-day snapshot with the conventions occurring during the second half of the chart.  Do you notice those drastic swings for and against Hillary Clinton and Donald Trump?  If not, you're not alone.  There is virtually no discernible change for either candidate.

For the record, as of July 31st, the betting markets favor Hillary Clinton winning the presidency over Donald Trump by a 68 to 35 margin (these are not percentages but rather how many cents it would cost per dollar to "Buy Yes" for each candidate).

So do the conventions matter?  According to the betting markets, not in the slightest.


Thursday, July 28, 2016

Basic Minecraft Programming Examples...

In the last post we reviewed how to set up Python for Minecraft.  Once you have it up and running, jump right in and try these two simple scripts that not only demonstrate what Python-for-Minecraft programs look like, but are also both really useful when you're actually playing.

Example 1:  Teleport to your starting location (or any location)

As most Minecrcaft newbies and veterans alike can tell you, sometimes you just want to go back to your "home base" (wherever in your world that might be) without needing to hike all the way there or even needing to figure out where it is.  Wouldn't it be helpful to have an easy way to teleport there whenever you wanted?

As soon as you start a new game, click on "F3" to see lots of in-game details display on the screen.  Pay particular attention to the three "Block" numbers (or XYZ numbers).  Then try this...

from mcpi.minecraft import Minecraft
mc = Minecraft.create()

# Replace these numbers with your three starting "Block" numbers

x = 20
y = 80
z = 16

mc.player.setTilePos(x, y, z)

Example 2:  Instantly build a shelter

Most players have to spend their first day gathering materials to build a shelter before nightfall.  But you can try this instead whenever you want to instantly build yourself a cobblestone shelter right next to your present location.  You can even build it in the sky!

# Get player's location and build a hollow cuboid

from mcpi.minecraft import Minecraft
mc = Minecraft.create()
pos = mc.player.getPos()

x = pos.x
y = pos.y
z = pos.z
width = 10
height = 5
length = 6
blockType = 4
air = 0

mc.setBlocks(x, y, z, x + width, y + height, z + length, blockType)
mc.setBlocks(x+1, y+1, z+1, x + width-1, y + height-1, z + length-1, air)

The result...


Wednesday, July 13, 2016

How to Set Up Python for Minecraft...

Most of the online tutorials on this topic seem weak, so here is my step-by-step beginner's tutorial on how to set up your Windows PC so that you can start writing Python programs for Minecraft.

Step #1:  Download and install Python (use any version after 3.5.0).  During installation, make sure to check the box to "Add Python to Path".

Step #2:  Download and install the Minecraft Python API and Spigot.  Go to this link and download the file: "Minecraft".  Extract all of the files, then double-click the file: "Install_API".

Step #3:  Run the Spigot Server.  Open the "Minecraft Tools" folder (that you extracted in Step #2), then double-click the file: "Start_Server".  Without closing the Spigot window, open Minecraft and choose "Multiplayer", then "Add Server".  In the server address box, type "localhost", then "Done".  Double-click the server name listed and your game should start.
  • If you have a compatibility issue when you double-click the server name, exit back to the Minecraft launcher and create a new profile. Edit the settings for that profile by selecting an older version release (for example, 1.9.4) 
You're now totally set up.  In the future, when you want to write the code for your programs, use IDLE (a text editor installed when you installed Python).  In order to execute your finished programs, make sure to run the Spigot server and Minecraft, and simply click "F5" within IDLE.

Some actual programming examples are coming soon.


Thursday, July 07, 2016

Using Tableau for Political Visualizations...

If you're curious about how Data Science is being used to analyze political data, look no further than Tableau - an easy-to-use tool for converting analytics into fabulous visualizations.

Recently, Tableau held a Politics Viz Contest to showcase some of the work their users are generating.  The contest winner, Robert Rouse, used data from a Pew Research Center report to visualize increasing political polarization in the U.S. over the past few decades (Click on the image to enlarge and see the details):

Or here is one that I personally found interesting - a visualization of how each state has voted in partisan terms since 1964:

Or this alternative visualization of state-by-state partisanship in every election since 1916:

Really, if the conjunction of Data Science and Politics is your jam, then it's worth checking out the entire Tableau gallery to see what people are doing in the field.


Tuesday, June 28, 2016

Brexit Voters Were Not Actually Googling "What is the EU?" After They Voted...

The day after the historic "Brexit" vote, where British citizens voted in a public referendum to leave the European Union, a headline from the Washington Post circulated wildly on social media:  "The British Are Frantically Googling What the E.U. Is, Hours After Voting To Leave It".

This was used to ridicule the validity of the results, insinuating that British voters had no idea what they were even voting for or against.  However, the headline is completely disingenuous.

Here's why.  The Washington Post reporter used Google Trends as the source of his information, and noted that the number of searches for the phrase "What happens if we leave the EU" had more than tripled in the hours after polls had closed.  But Google Trends reports search numbers in relative terms and within the date range and context of other trends.

So, for example, the "250% spike" in searches for the E.U. "in the past hour" only means that the number of searches more than tripled relative to the number of searches in the previous hours.  More importantly, it says absolutely nothing about the total number of searches actually made.

What this means is that a very small number of people conducting their searches can lead to Google Trends (or, more accurately, a journalist using Google Trends) completely misrepresenting how many people are doing those searches.  For instance, if 10 people had searched that phrase in the previous hour, and now 25 people searched that phrase in the current hour, then Google Trends would note that it was a 250% increase.  Although, clearly, 25 people hardly represent the entire U.K. with its tens of millions of people.

And, in fact, this is what happened the other night.  As Remy Smith uncovered, in the month leading up to the vote, that phrase was receiving about 261 searches per day.  Even after tripling, that still means that fewer than 1,000 individuals actually googled “What is the EU” in response to the “Leave” victory. And that is hardly enough to conclude that large swaths of the population were generally uninformed.

As if any more context were needed, consider that more people googled "Game of Thrones" that night more than "What is the EU?".  By a lot.


Thursday, May 05, 2016

Results for the Presidential 'Facebook Primary'...

Now that Donald Trump is officially the presumptive Republican nominee and Hillary Clinton is basically, if not yet officially, the same on the Democratic side, it might be instructive to save, for posterity's sake, the results of Nate Silver's "Facebook Primary" data.

First, let's look at the Democratic campaign.  In terms of raw Facebook "likes", Bernie Sanders beats Hillary Clinton nationwide by a nearly 3-to-1 margin.  Yet Hillary Clinton is currently leading Bernie Sanders in the popular vote by more than 3 million votes and a 56%-42% margin.

Number of raw Facebook "likes" for each candidate, by county (Sanders in purple, Clinton in green)...

Another interesting metric is which candidates perform best on Facebook by county relative to their national share of "likes".  In other words, this map shows each candidates' strongholds of support (Sanders in purple, Clinton in green)...

Next, let's look at the Republican campaign, where, in terms of raw Facebook "likes", Donald Trump has more than all other candidates combined (Trump in orange, Cruz in red, and no other candidate registers enough likes to be on the map)...

And, again, here's a map showing each candidates' strongholds of support (Trump in orange, Cruz in red, Kasich in purple)...

The million-dollar question, of course, is to what extent Facebook can be a predictor of electoral success.  Based on the numbers, it hasn't been a very good predictor in the Democratic race, but has been a good one in the Republican race.  That's a ratio of 1:2, or a 50% rate of accuracy.  You don't need to be a statistician to see how 50% accuracy in predicting elections essentially means that it's not accurate at all.


Monday, March 21, 2016

CUNY's Women in Technology Initiative...

We all hope that on some rare occasion we will be fortunate enough to work on something more meaningful, more impactful, than the day-to-day routine aspects of our jobs.  This past January I was fortunate enough to be given the opportunity to co-teach a pilot course with Professor Zachary Dodds that aimed to tackle the challenge of why there are so few female students in Computer Science, and then set out to do something about it.  This effort has now been officially launched as the Women in Technology and Entrepreneurship in New York Initiative.

Please check out the good work the initiative is pursuing at the link above, and check out the video embedded here - you'll not only watch video testimonials from our former students (who represent themselves extremely well, if I may say so), but you'll also catch a fleeting glimpse of yours truly giving a lecture.

It is an honor to be part of this program, and it's the type of thing that I'm grateful that my kids will one day see that I was a part of.


Thursday, March 17, 2016

Who is Anonymous Fighting For in Their War Against Donald Trump?

The hacker group Anonymous has declared "total war" on Donald Trump, urging hackers to expose personal information about him and his staff and to disable various websites associated with him including,,,, etc.

Even though #OpTrump mentions April 1st as its target date, the collective has already posted unverified personal information about the candidate and his staff, including social security numbers.  Trump was also previously targeted by Anonymous last December following his comments about banning Muslims from entering the country.

The reason for the "call-to-arms", as stated in the Anonymous YouTube video, is that “Your inconsistent and hateful campaign has not only shocked the United States of America, you have shocked the entire planet with your appalling actions and ideals".

This action shouldn't surprise anyone because this is basically what Anonymous does.  What's interesting is that this time, rather than go after targets affiliated with the Ku Klux Klan or ISIS, or even Mastercard or Visa, as they have done in the past, they're going after an American presidential candidate who's currently in the process of winning the nomination based on the popular vote.

You don't need to be a Trump supporter to take issue with the fact that, whereas in the past Anonymous has chosen institutional targets like multi-national corporations or terrorist organizations, here they are going after an individual who is being democratically elected.  Rather than being an agent of The People, as they like to perceive themselves, they are actually fighting against what The People are voting for (on the Republican side).

Is Anonymous exposing itself as ideologically liberal, or perhaps even outright partisan?  If they seek to fight and resist the valid results of democratic elections then Anonymous is now showing itself to be anti-democratic in addition to anti-capitalist and anti-free speech (at least, anti-any-speech-that-they-disagree-with).  If that's the case, can someone please explain who exactly they are fighting for?


Thursday, March 03, 2016

The Python Elections Library and Other Programming Tools for Election Analysis...

During this campaign season, many political scientists are conducting research experiments that use online datasets as their primary units of analysis.  For instance, in this blog's previous post, real-time Twitter data was collected and then analyzed to get a sense of popular sentiment about Chris Christie's endorsement of Donald Trump.  Scraping data from social media sites is an increasingly common focus of statistical research.

To this end, there are numerous programming tools available to political scientists.  Let me start by saying that, personally, my language of choice for tasks of this sort has long been Java, although this year I've migrated over to Python.  Meanwhile, Web APIs have been around for years so that researchers can easily access the data on Twitter, Facebook, Google, Reddit, etc. Also, for this 2016 election cycle there are a few notable new additions to our collective toolkit that, combined with some useful already-existing tools, really improve a researcher's capabilities.  Here's what I'm using...

  • Tweepy - still the best Python-based library for accessing the Twitter API.
  • Alchemy and NLTK - the most common APIs for language sentiment analysis.
  • The Python Elections Library - a pay-for library that provides access to all federal, state, and local election results, as well as delegate estimates for the presidential nomination contests.
  • Elex - the Associated Press' brand new command-line tool for accessing current election data.
  • The Watson Emotion Analysis API - IBM's brand new API that was just released in Beta. Whereas Alchemy analyzes language sentiment as positive, negative, or neutral, the Emotion Analysis API detects joy, fear, anger, sadness, and disgust, and rates them by order of magnitude.
  • Matplotlib - the common Python library for visualizing the data with charts and graphs (although I'm currently searching for a better visualization tool, if you have recommendations).

These tools are a great starting point for collecting and analyzing social media data.  Now if only you didn't have to pay a fee for some of these large datasets, or for a sentiment analyses of them, then we'd really be cooking.


Friday, February 26, 2016

What Do Twitter Users Think of Trump Being Endorsed by Christie?

Just an hour or so ago, New Jersey Governor Chris Christie publicly endorsed Donald Trump for president.  This is turning a lot of heads in pundit circles, however I wanted to just do a quick analysis of what people were saying about the endorsement on Twitter.

Running a python script of my own creation, I scraped 949 tweets referring to Trump over the course of a single minute (approximately an hour after the public endorsement).  So this is neither a scientific nor a meaningfully large sample size by any means; rather, I just wanted to get a quick snapshot.  All 949 tweets were then run through IBM's Alchemy engine for sentiment analysis so that we could get a sense of whether tweets about Trump were singing his praises, condemning him, or staking out a more neutral stance.  Here are the data collected:

  • Number of tweets analyzed:  949
  • Number of positive tweets:  254
  • Number of negative tweets:  343
  • Number of neutral tweets:  305

Which can be visualized as follows:

Just some real-time API-driven sentiment for your consumption  :-)


Sunday, February 21, 2016

Apple Challenges the Federal Court Ruling That It Must Create New Unlocking Software...

Yes, Apple and the federal government are at war with each other. In case you missed it, this week a federal court ordered Apple to unlock the iPhone of one of the San Bernardino terrorists who killed 14 people, and Apple is strongly challenging that ruling. The reasons why strike at the very heart of, not only digital privacy rights (which is how many are framing it), but also what constitutional checks will there be on the power of the federal government moving forward.

The center of this controversy is not whether the court can compel Apple to unlock an iPhone to provide user data to law enforcement authorities (it's legally clear that they can with a subpoena, and Apple has already done so with all of the San Bernardino shooter's data stored on iCloud); but the real issue is whether the court can compel Apple to create new software.

It comes across as absurd that the stated legal basis for the court's ruling is the All Writs Act of 1789, which is used to require people or businesses not involved in a case to execute court orders.  I mean, come on.  Using a law from 1789 to judge a case on digital encryption and software development severely damages the ruling's credibility in the public's eye, to say the least.  One could almost feel the palpable collective eyeroll most Americans had upon hearing this.

The fact is, despite the All Writs Act of 1789, the Supreme Court has ruled that "the government cannot compel a third-party that is not involved in a crime to assist law enforcement if doing so would place 'unreasonable burdens' on it".  Forcing Apple to create brand new software seems to be just such an unreasonable burden.

Of even more direct relevance is the case Bernstein v. Department of Justice, where a college student challenged a federal law that classified strong encryption tools as a form of arms munitions, and therefore forced him to register as an arms dealer in order to publish his encryption algorithm.  In 1999, a judge ruled that the government cannot regulate cryptographic "software and related devices and technologies" because doing so would violate First Amendment protections of free speech on the grounds of prior restraint.

All of Silicon Valley as well as the hacktivist community at-large has come out in support of Apple's legal challenge.  To what extent should the government be able to force third-party companies or individuals to, not just hand over subpoenaed information, but actually be forced to create new products to assist the government in carrying out its duties?  We all want to be safe and assisting law enforcement in preventing attacks is clearly a public good.  Again, Apple has already done that by handing over all of the data on the shooter they had access to.  But a requirement to force a company to create new software (which, by the way, would be the equivalent of forcing the company to make an inferior product because it would turn all iOS products into less secure devices and, by doing so, would severely damage the company's commercial reputation)?  The line has to be drawn somewhere.


Monday, February 08, 2016

When Hacktivism Should Receive First Amendment Protection...

I recently came across an article by Noah C. N. Hampson titled, "Hacktivism: A New Breed of Protest in a Networked World", in the Boston College International and Comparative Law Review.  It's a great entry piece for anyone looking to become acquainted with the issue of hacktivism in relation to the First Amendment.

Hampson's main argument is that some forms of hacktivism are primarily expressive, not destructive, and that these acts sufficiently resemble traditional forms of protest enough to warrant protection from anti-hacking laws under widely accepted principles of Free Speech.  Specifically, he calls out the Computer Fraud and Abuse Act in the U.S. for being so generally worded as to be dangerous in its potentially overly broad application.

He goes on to make a several noteworthy comments:
  1. That "time, place, and manner" restrictions on the Internet are unclear.  These are the laws that allow the authorities to determine when and how protests can occur (knowing they cannot censor or ban them altogether) in order to maintain public safety.

  2. The "public forum doctrine" on the Internet is also unclear.  This generally protects speech in "places by which long tradition or by government fiat have been devoted to assembly or debate".

  3. The distinction between public versus private spaces on the Internet is often blurred.  Again, this relates to the different bodies of law that have developed over time regarding the regulation of speech when it occurs on public versus private property.

  4. It does not follow that if ANY harm is caused, then the act should be considered criminal;  some costs (inconveniences/annoyances) must be tolerated as the price for freedom of expression.

These are all important points to raise.  I would like to contribute the observation that all of the first three points are really about the public versus private distinction (or lack thereof).  The "time, place, and manner" of a hacktivist protest cannot be physically relocated the way a planned protest at Madison Square Garden in New York City can be relocated by the authorities to Riverside Park.  On the Internet, that physical dimension is meaningless, and the hacktivist target, by definition, is a server that is most likely a private server, rather than a public sidewalk.  The question is: since the overwhelming majority of cyberspatial activities occurs on private servers, to what extent can First Amendment protections that traditionally apply to public spaces still be valid?  For if hacktivism is deemed to only have First Amendment protections when applied to strictly public spaces, you might as well throw out the notion of Free Speech on the Internet altogether.

Which brings us to Hampson's second point which is a foundational question:  To what extent is the Internet a public or a private space at all?  Yes, its functional definition is that it is a network of mostly private networks, which would indicate that it ought to be legally considered a private space in the majority of cases.  However, even though I may connect to the Internet through my private ISP and then my traffic or request for data might at some point traverse the private backbone of a provider like Cogent, all so that I can access my account on one of Facebook's privately owned servers in Silicon Valley, the sum is often considered greater than its parts when it comes to understanding how this thing called the Internet works.  When I engage in cyberspace, I'm typically using only private resources to do what I do, yet there's an argument to be made, and certainly an enormous public perception, that many of these publicly accessible forums are indeed public spaces even if the resources that facilitate them are not.  The First Amendment applies differently to public spaces than to private spaces.  So we might be in need of legal clarification as to the Internet's hybrid status before we can determine its applicability to hacktivism.

Finally, as to Hampson's fourth point listed above, I'm inclined to disagree with his argument that even some harmful acts of hacktivism should warrant First Amendment protection.  Anti-hacking laws such as the CFAA are designed to prevent unlawful intrusions, destruction, and theft.  Yes, perhaps they are too generally worded and in need of revision.  But I'm unsure of what type of "harm" exactly that Hampson is referring to that ought to be tolerated.  He discusses website defacements, denial-of-service attacks that effectively shut down target websites, and other hacktivist tactics, although even after reading the article I find myself confused as to why he believes some of these are "harmful" while others are not.  What test should be applied?

Anyway, this is terrific food for thought.  I'm left questioning the public-private debate and how best to define "harm" in a hacktivist context.  All First Amendment considerations will stem from these starting assumptions.


    Sunday, January 24, 2016

    Yet Another "Has the Bitcoin Experiment Failed?" Post...

    Bitcoin is facing an existential crisis.  Two weeks ago, in one of the most stunning op-eds by an insider you might ever come across, Mike Hearn announced his resignation from the Bitcoin project and stated how he has come to, in no uncertain terms, "the now inescapable conclusion that [Bitcoin] has failed".

    Past tense.

    On the surface, it might seem that the problem is the 1 MB block size limit, a technical measure which affects the number and speed of transactions that Bitcoin can be handle.  However, that can be changed.  The real issue, as Hearn sees it, is "people problems".

    There are currently two competing visions for Bitcoin - one in which Bitcoin expands its commercial potential and gains mainstream usage; another in which it challenges the existing global financial system.  Or, as Fred Wilson told Business Insider, "Should Bitcoin be Gold or should Bitcoin be Visa? If it is Gold, it’s a store of wealth and something to peg value to. If it is Visa, then its a transactional network that can move wealth around the globe in a nanosecond."

    Hearn describes that the camp in favor of raising the block size limit, known as the Bitcoin XT camp, which includes himself, has been the target of cyberattacks in recent months and that their views have been censored on online discussion boards.

    He also points out that "the block chain is controlled by Chinese miners, just two of whom control more than 50% of the hash power. At a recent conference over 95% of hashing power was controlled by a handful of guys sitting on a single stage...  Bitcoin has no future whilst it’s controlled by fewer than 10 people".  This, of course, begs the question: What has happened to the vision of a decentralized currency?

    Meanwhile, in the storm of fury that came in reaction to Hearn's revelatory post, conspiracy theories have begun circulating across the Internet highlighting the fact that Hearn has since accepted a job at R3 - a startup backed by some of the biggest financial institutions around, including Morgan Stanley, Goldman Sachs, JP Morgan, and UBS.  There is a clear insinuation that this entire episode is "some sort of banker conspiracy".

    Meanwhile, not be be considered an afterthought, the value of Bitcoins plummeted on the very day Hearn's post was published, but has since began a gradual recovery:

    In the week-and-a-half since, much has been written about Bitcoin's demise, and even death.  This is probably an overreaction.  As former Wall Street Journal reporter Daniel Palmer summed it up, the big idea of a "software-driven governance system" is very much alive, however the possibility of Bitcoin becoming a mass-adopted currency is dead, and it simply won't become "a replacement for the dollar" anytime soon.

    That's the analysis that rings most true here as well.  As this existential dispute among Bitcoin's core developers comes to light, it would seem foolish for anyone to start trading in their dollars or euros for satoshis, but it would also seem equally foolish to completely disregard the fact that Wall Street firms have seriously begun adopting the blockchain approach to transaction-handling in clearinghouses and elsewhere.

    The idea still has a bright future, even if my CoinBase account does not.


    Saturday, January 16, 2016

    A Few Takeaways from My Reddit AMA...

    Last week I had the opportunity to do an AMA (Ask Me Anything) spotlight on Reddit in the /r/Politics subsection.  Here is a link to the entire transcript.  Now that a few days have passed, here are some observations for anyone who may be considering something similar going forward.
    • The participating audience asked many well-informed intelligent questions and the replies were generally respectful and civil in tone.  I think people's biggest fear from doing such online Q&As is the fear of trolls and having the dialogue break down towards the least common denominator.  That was not the experience here.  Reddit's userbase quite impressed me, actually.
    • "Ask Me Anything" should be taken literally.  The very first question I was asked was personal and straight to the point: "Who is your favorite presidential candidate right now?".  Indeed, the entire line of questioning ranged from identifying my personal politics, to where I work, to my positions on different issues, and much more.  Which was fine.  But anyone thinking that they'd be able to stick to a script of some type - academic, professional, etc. - and be evasive in the face of personal questions should reconsider whether an AMA is a good fit for them.
    • Don't expect any monetization.  Admittedly, I was originally motivated to do the AMA as a way of marketing my book.  I made sure to follow the site's recommendation not to push your sales pitch too hard, but I still included my book's title in the AMA title and also posted a link to my blog.  In the end, despite the /r/Politics subreddit having over 3 million subscribers, several thousand active online during the AMA, the AMA receiving 194 points and 178 comments, it all resulted in just one book sold through Amazon.  On the other hand, my blog did roughly triple its usual amount of traffic for the week and I acquired a healthy number of new subscribers. The takeaway here: AMAs are good for expanding your audience, but not so much for sales.
    Just some food for thought.  But, overall, it was a surprisingly positive experience.


    Wednesday, November 25, 2015

    A Facebook Voting Model for Predicting Elections...

    The race has been on for the past several years for scholars to figure out exactly how to best use social media metrics to help predict electoral outcomes.  Matthew MacWilliams is the most recent entrant into the fray.  He recently published an article in Political Science and Politics in which he proposes a new model for forecasting elections incorporating Facebook data - specifically, the number of Facebook "Likes" a candidate's page receives as well as Facebook's "People Talking About This" (PTAT) statistic.

    For those of you who may be skeptical of a purely Internet effect on voting behavior, these were not the only variables used.  Rather, MacWilliams factored in the Facebook participation variable along with other well-established metrics that also track partisanship and incumbency advantage.

    The results:  Testing the model against 15 of the most competitive Senate races in 2012, the Facebook model proved to more accurately predict the Senate winner in 6 of the 8 weeks leading up to Election Day than the fundamentals model that does not include Facebook data.  Additionally, it proved more accurate than "polls-of-polls" forecasts in 5 of those 8 weeks.

    So this new Facebook model seems great, right?  Well, while the research is solid and the experiment thoughtfully constructed, a few questions are dying to be answered.

    For starters, why is Facebook the sole focus of the study, and why only those two metrics?  I understand that Facebook data is methodologically more available and accessible than Big Data datasets, including Twitter, but that alone does not make Facebook the ideal choice for representing the dynamics of what's happening across all of social media.  Different sites have unique characteristics, and thus can skew results.  Granted, something had to be chosen, but ideally some cross-section sample of a number of social media sites might be more instructive.

    Second, when compared to the poll-of-poll averages, the Facebook model was better at forecasting in 5 of the 8 weeks.  Yes, that's more accurate, but 5-3 is barely so, and it's not exactly a resounding confirmation.  Also, as MacWilliams himself points out, the Facebook model was better at forecasting outcomes the farther the prediction was from Election Day.  In other words, the closer to Election Day, the less accurate it was.  WHY?  That's an extremely interesting footnote - and surprisingly counter-intuitive considering that one would expect the online metric to more accurately reflect people's sentiments in real-time.

    Finally, the article closes by mentioning its "grand prize" goal:  to be useful in predicting presidential election outcomes.  However, to me it may be more interesting to determine how the Facebook model might hold up when applied to state/local elections?  Because of lower voter turnout and less engagement, generally, in local elections, I could almost make the argument that measuring Facebook "Likes" and the number of "People Talking About This" could either confirm the model's accuracy or completely invalidate it.  On the local level, it seems like it could go either way, which is why it would be great to see an experiment done on that front.

    Overall, this research study was well done in a subfield where more experiments desperately need to be conducted.  For those readers out there not completely sold on the two Facebook metrics used, what alternative social media metrics do you think might be better?