champagne anarchist | armchair activist

Trick the trackers with a flood of meaningless data

A couple years ago, Apple obtained a patent for an intriguing idea: create a fake döppelganger that shares some characteristics with you, say birth date and hair colour, but with other interests - say basket weaving. A cloning service would visit and interact with websites in your name, messing up the profile companies like Google and Facebook are keeping of you.

I don’t think anyone has implemented it. But now I read at Mathbabe’s blog about a similar idea that actually has been implemented. It’s called Noiszy and it is

a free browser plugin that runs in the background on Jane’s computer (or yours!) and creates real-but-meaningless web data – digital «noise». It visits and navigates around websites from within the user’s browser, leaving your misleading digital footprints wherever it goes.

Cool project. However, it has been argued that the organisations that are tracking you can easily filter out the random noise created by Noiszy.

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My failed attempt to build an interesting twitter bot

In 2013, I created @dataanddataviz: a twitter account that retweets tweets about data analysis, charts, maps and programming. Over time, I’ve made a few changes to improve it. And @dataanddataviz did improve, but I’m still not satisfied with it, so I decided to retire it.

There are all sorts of twitter bots. Often, their success is measured by how many followers they gain or how much interaction they provoke. My aim was different. I wanted to create an account that is so interesting that I’d want to follow it myself. Which, of course, is a very subjective criterion (not to mention ambitious).

Timing

First a practical matter: it has been suggested that you can detect twitter bots by the timing of their tweets. The chart below (inspired by this one) shows the timing of posts by @dataanddataviz.

I randomized the time at which @dataanddataviz posts. The median time between tweets is about 100 minutes (I lowered the frequency last January, as shown by the dark green dots). There is no day/night pattern. If tweets were posted manually, you’d expect a day/night pattern.

Selecting tweets

Initially, I collected tweets using search terms such as dataviz, data analysis and open data. From those tweets, I tried selecting the most interesting ones by looking how often they had been retweeted or liked. @dataanddataviz would retweet some of the more popular recent tweets.

This was not a success. For example, there are quite a few people who tweet conspiracy theories and include references to data and charts as «proof». Sometimes, their tweets get quite a few likes and retweets, and @dataanddataviz ended up retweeting some of those tweets. Awkward.

I decided to try a different approach: follow people who I trust, and use their retweets as recommendations. If someone I trust thinks a tweet is interesting enough to retweet, then it may well be interesting enough for @dataanddataviz to retweet.

The people who I follow tweet about topics like data and charts, but sometimes they tweet about other topics too. To make sure tweets are relevant, I added a condition that the text of the tweet contains at least one «mandatory» term (e.g. python, d3, or regex). I also added a condition that none of a series of «banned» terms was in the text of the tweet. I used banned terms for two purposes: filter out tweets about job openings and meetings (hiring, meetup) and filter out hypes (bigdata, data science).

This approach was a considerable improvement, but I still wasn’t happy. Sure, most of @dataanddataviz’ retweets now were relevant and retweets of embarrassing tweets became rare. But too few retweets were really good.

Predict quality?

I tried if I could predict the quality of tweets. I created an interface that would let me rate tweets that met the criteria described above: retweeted by someone I follow; containing at least one of the required terms and containing none of the banned terms.

The interface shows the text of the tweet and, if applicable, the image included in it, but not the names of the person who originally posted the tweet and the recommender who had retweeted it. This way, I forced myself to focus on the content of the tweet rather than the person who posted it. Rating consisted in deciding whether I would retweet that tweet.[1]

I rated 1095 tweets that met the basic criteria. Only 130 were good enough for me to want to retweet them. That’s not much.

I looked if there are any characteristics that can predict whether a tweet is - in my eyes - good enough to retweet. For example: text length; whether the text contains a url, mention or hashtag; and characteristics of the person who originally posted the tweet, such as account age; favourites count; followers count; friends count; friends / followers ratio; statuses count and listed count. None of these characteristics could differentiate between OK tweets and good tweets.

I also looked whether specific words are more likely to appear in good tweets - or vice versa. This was the case, but most examples are unlikely to generalise (e.g., good tweets were more likely to contain the word air or #demography).

Conclusion

I didn’t succeed in creating a retweet bot I’d want to follow myself. @dataanddataviz’ retweets are generally OK but only occasionally really good.

Also, I couldn’t predict tweet quality. Perhaps it would make a difference if I used a larger sample, or more advanced analytical techniques, but I doubt it. Subjective quality appears to be difficult to predict - which shouldn’t come as a big surprise (in fact, Twitter itself isn’t very good at predicting which tweets I’ll like, judging by their You might like suggestions).

Meanwhile, I found that since November, more of the tweets retweeted by @dataanddataviz tend to have a political content. Retweeting political statements isn’t something I want to delegate to a bot, so that’s another reason to retire @dataanddataviz.


  1. Obviously, what is being measured is a bit complicated. Whether I’d want to retweet a tweet depends not only on its quality, but also on its subject. For example, I’m now less inclined to retweet tweets about R than I was a couple years ago, because I started using Python instead of R.  ↩

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Quitting Facebook

Last month, data scientist Vicki Boykis posted an interesting article about the kind of data Facebook collects about you. It’s one of those articles that make you think: I really should delete my Facebook account - and then you don’t.

One could argue that Google search data illustrates how people relate to Facebook. People know Facebook isn’t good for them, but they can’t bring themselves to quit. However, when it’s time for New Year’s resolutions, they start googling how to delete their account.

UPDATE - Vicki Boykis just suggested to label major news events. In the past Google Trends had a feature that did just that, but I think they killed it. Of course, you can still do Google or Google News searches for a particular period. As a start I added two stories that may have contributed to the mid–2014 peak. Let’s see if other people come up with more.

Method

Note that the Google search data is per week so each data point really refers to the week starting at that date.

I wanted to do a chart like this in December last year, which would perhaps have been a more appropriate moment. However, I didn’t get consistent data out of Google Trends using search terms like quit facebook. The other day, after deleting my own Facebook account, I realised I had probably used the wrong search term. People don’t search for quit facebook but more likely for delete facebook - they’re looking for technical advice on how to delete their account.

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New Python package for downloading and analysing street networks

stationsplein

The image above shows square mile diagrams of cyclable routes in the area around the Stationsplein in Amsterdam, the Hague, Rotterdam and Utrecht. I made the maps with OSMnx, a Python package created by Geoff Boeing, a PhD candidate in urban planning at UC Berkeley (via).

Square mile diagrams are a nice gimmick (with practical uses), but they’re just the tip of the iceberg of what OSMnx can do. You can use it to download administrative boundaries (e.g. the outline of Amsterdam) as well as street networks from Open Street Map. And you can analyse these networks, for example: assess their density, find out which streets are connections between separate clusters in the network, or show which parts of the city have long or short blocks (I haven’t tried doing network measure calculations yet).

Boeing boasts that his package not only offers functionality that wasn’t (easily) available yet, but also that many tasks can be performed with a single line of code. From what I’ve seen so far, it’s true: the package is amazingly easy to use. All in all, I think this is a great tool.

Amsterdam’s most irritating traffic light is at the Middenweg

Red and orange dots show locations of irritating traffic lights. If any comments have been submitted, the dot is red. Click on a red dot, or type a few letters below, to see comments about a particular crossing (comments are mostly in Dutch).

Amsterdam’s most irritating traffic light is at the crossing of Middenweg and Wembleylaan, according to a poll among cyclists. The Amsterdam branch of cyclists’ organisation Fietsersbond says the top 10 most irritating traffic lights are well-known problem sites.

Comments made by participants in the poll show that cyclists are not just annoyed about long delays; they are also concerned about safety, especially at locations where many (school) children cross the street. Some cyclists nevertheless keep their spirits up: Plenty of time for an espresso there!!

Here are the ten most irritating traffic lights:

  1. Middenweg / Wembleylaan
  2. Amstelveenseweg / Zeilstraat
  3. Middenweg / Veeteeltstraat
  4. Rozengracht / Marnixstraat
  5. Meer en Vaart / Cornelis Lelylaan Nz
  6. IJburglaan / Zuiderzeeweg
  7. mr Treublaan / Weesperzijde
  8. Frederiksplein / Westeinde
  9. Nassauplein / Haarlemmerweg
  10. Van Eesterenlaan / Fred Petterbaan

Some are at routes where the city gives priority to car circulation, at the expense of cyclists and pedestrians. However, cyclists say they frequently have to wait at red lights even though the crossing is empty. This could be a result of budget cuts on maintenance of the systems that detect waiting cyclists.

Quite a few cyclists complained about cars running red lights (perilous!) or blocking the crossing. Further, not everybody is happy with crossings where all cyclists simultaneously get a green light. Such a set-up is nice if you have to make a left turn, for it will spare you having to wait twice, but it may result in chaos.

The Fietsersbond wants traffic lights adjusted to create shorter waiting times for cyslists and pedestrians. Research by DTV consultants found that adjusting traffic lights is a simple and cheap way to improve the circulation of cyclists, and that it also improves safety.

An analysis of location data from cyclists’ smart phones found that there are traffic lights in Amsterdam where the average time lost exceeds 30 seconds.

Thank you to the Fietsersbond and to Eric Plankeel for their input; and to all cyclists who participated in the poll.

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