Surprising Stats From Mining One Million Tweets About #Syria
I’ve been filtering Twitter’s firehose for tweets about “#Syria” for about the past week in order to accumulate a sizable volume of data about an important current event. As of Friday, I noticed that the tally has surpassed one million tweets, so it seemed to be a good time to apply some techniques from Mining the Social Web and explore the data.
While some of the findings from a preliminary analysis confirm common intuition, others are a bit surprising. The remainder of this post explores the tweets with a cursory analysis addressing the “Who?, What?, Where?, and When?” of what’s in the data.
If you haven’t been keeping up with the news about what’s happening in Syria, you might benefit from a piece by the Washington Post entitled 9 questions about Syria you were too embarrassed to ask as helpful background knowledge.
Filtering the Firehose
In addition to an introduction for mining Twitter data that’s presented in Chapter 1 (Mining Twitter) of Mining the Social Web, 2nd Edition, a cookbook of more than two dozen recipes for mining Twitter data is featured in Chapter 9 (Twitter Cookbook.) The recipes are fairly atomic and designed to be composed as simple building blocks that can be copied, pasted, minimally massaged in order to get you on your way. (In a nutshell, that’s actually the purpose of the entire book for the broader social web: to give you the tools that you need to transform curiosity into insight as quickly and easily as possible.)
You can adapt concepts from three primary recipes to filter and archive tweets from Twitter’s firehose:
- Accessing Twitter’s API for Development Purposes (Example 9-1)
- Saving and Accessing JSON Data with MongoDB (Example 9-7)
- Sampling the Twitter Firehose with the Streaming API (Example 9-8)
Although there is a little bit of extra robustness you may want to add to the code for certain exceptional circumstances, the essence of the combined recipes is quite simple as expressed in the following Python code example:
import twitter import pymongo # Our query of interest q = '#Syria' # See Example 9-1 (Accessing Twitter's API...) for API access twitter_stream = twitter.TwitterStream(auth=twitter.oauth.OAuth(...)) # See https://dev.twitter.com/docs/streaming-apis for more options # in filtering the firehose stream = twitter_stream.statuses.filter(track=q) # Connect to the database client = pymongo.MongoClient() db = client["StreamingTweets"] # Read tweets from the stream and store them to the database for tweet in stream: db["Syria"].insert(tweet)
In other words, you just request authorization, filter the public stream for a search term, and stash away the results to a convenient medium like MongoDB. It really is that easy! In just over a week, I’ve collected in excess one million tweets (and counting) using exactly this approach, and you could do the very same thing for any particular topic that interests you. See the IPython Notebook featuring the Twitter Cookbook for all of the finer details.)
As discussed at length in Chapter 1 of Mining the Social Web, there is roughly 5KB of metadata that accompanies those 140 characters that you commonly think of as a tweet! A few of the metadata fields that we’ll leverage as part of exploring the data in this post include:
- Who: The author’s screen name and language
- What: Tweet entities such as #hashtags, @mentions, and URLs
- Where: Geo-Coordinates for where the tweet was authored
- When: The date and time the tweet was authored
Of course, there are some other details tucked away in the 5KB of metadata that could also be useful, but we’ll limit ourselves to just using these fields for this post. The goal is just to do some initial exploration and compute some basic statistics as opposed to compute anything scholarly or definitive.
…there is roughly 5KB of metadata that accompanies those 140 characters that you commonly think of as a tweet…
The remainder of this section presents some of the initial findings from mining the data with Python and techniques from the social web mining toolbox.
The underlying frequency distribution for the authors of the tweets reveals that there just over 305,000 accounts that have contributed the 1.1 million tweets in the data set. The underlying frequency distribution shown below reveals a long tail with certain accounts at the head of the curve contributing highly disproportionate numbers to the overall aggregate.
A closer inspection of the accounts contributing disproportionate numbers of tweets reveals that the top accounts (such as RT3Syria as shown below) appear to be bots that are retweeting anything and everything about Syria. This finding makes sense given that it is unlikely that any human being could author hundreds of meaningful tweets a day.
On the chart, notice that at around the 100,000th rank, the number of tweets per author reaches one, which means that 200,000 of the 1.1 million tweets are accounted for by 200,000 unique accounts while the other 900,000 tweets are shared amongst the remaining 100,000 accounts. In aggregate, this means that about 80% of the content is accounted for by one-third of the contributing accounts!
A table below displays the frequency information for any screen name contributing more than 1,000 tweets in case you’d like to further investigate these accounts that sit at the head of the curve.
Another interesting aspect of exploring who is contributing to #Syria tweets is to examine the language of the person who is tweeting. The following chart show that the vast majority of the tweets are written in English and Arabic. However, an separate breakdown excluding English and Arabic is also provided to gist the representation from other languages.
For curiosity’s sake, the following table conveys frequency information for any language that appeared more than 100 times across the 1.1 million tweets.
Given the nature of world news and that the spotlight has very much on the United States this past week, it is not surprising at all to see that English is by far the dominant language. What is useful about this exercise, however, is to be able to provide a quantitative comparison between the number of tweets authored in English and Arabic. It is also a bit surprising to see that other widely spoken languages such as Spanish appear with such low frequency. A closer investigation of the English tweets might be worthwhile to look for traces of “Spanglish” or other mixed language characteristics.
One of the more useful pieces of metadata that is tucked away in a tweet is a field with the tweet entities such as hashtags, user mentions, and URLs nicely parsed out for easy analysis. In all, there were approximately 44,000 unique hashtags (after normalizing to lowercase) with a combined frequency of 2.7 million mentions that included #Syria (and common variations) itself. There were over 64,000 unique screen names and more than 130,000 unique URLs appearing in the tweets. (It may be possible that there is actually a lesser number of unique URLs since many of the URLs are short links that might resolve to the same address. Additional analysis would be required to make this determination.)
This chart conveys frequencies for the top 100 tweet entities for each category to show the similarity in the characteristics of the distributions.
Additionally, the following column-oriented table presents a compact view of the top 50 tweet entities for each category that you can review to confirm and challenge intuition about what you’d suspect to be the most frequently occurring tweet entities. (Note that there is no correlation for the items grouped in each row besides the row number itself, which corresponds to overall rank. The format of this table is purely to provide a compact view of the data.)
|Hashtag||Hashtag Freq||Screen Name||Screen Name Freq||URL||URL Freq|
It isn’t surprising to see variations of the hashtag “Syria” (including the Arabic translation سوريا) and screen names corresponding to President Obama along with other well-known politicians such as Speaker John Boehner, Senators John McCain and Rand Paul, and Secretary of State John Kerry at the top of the list. In fact, the appearance of these entities is one of the most compelling things about this analysis: it was generated purely from machine readable data with very little effort and could have been completely automated.
…the appearance of these entities is one of the most compelling things about this analysis: it was generated purely from machine readable data with very little effort and could have been completely automated…
The tweet entities, including URLs is remarkably fascinating and well worth some extra attention. Although we won’t do it here, a worthwhile followup exercise would be to summarize the content from the webpages and mine it separately by applying Example 24 (Summarizing Link Targets) from Chapter 9 (Twitter Cookbook). These web pages are likely to be the best sources for sentiment analysis, which is one of the holy grails of Twitter analytics that can be quite tricky to detect from the 140 characters of the tweets themselves.
Of the 1.1 million tweets collected, only approximately 0.5% (~6,000) of them contained geo-coordinates that could be converted to GeoJSON and plotted on a map. The image below links to an interactive map that you can navigate and zoom in on the clusters and view tweet tweet content by geography. You can read more about how the interactive visualization was constructed in a previous post entitled What Are People Tweeting About Syria in Your Neck of the Woods?
Although only 0.5% of all tweets containing geo-coordinates may sound a bit low, bear in mind the previous finding that certain “retweet bot” and news accounts are generating massive amounts of content and will probably not include geo-coordinates. (Somewhere on the order of 1% of tweets containing geo-coordinates is also consistent with other analysis I’ve done on Twitter data.)
What is a bit surprising about the geo-coordinates (but then starts to make some amount of sense) once you take a closer look is that there is a small number of geo-enabled accounts that generate a disproportionate amount of content just as there were with the larger aggregate as we earlier observed.
Try zooming in on the area around Berkeley, California, for example, and you’ll notice that there is one particular account, @epaulnet, that is mobile and generated virtually all of the content for the cluster in that region as shown below.
A chart displaying the proportionality of each geo-enabled account relative to the frequency of tweets that it produces is shown below and consistent with previous findings. It is a bit surprising that the amount of content generated by accounts with geo-coordinates enabled is highly skewed. However, it starts to make some sense once you consider that it conforms to the larger aggregate population.
Although omitted for brevity, it is worth noting that the timestamps in which the geo-enabled content was produced also correlated to the larger population in which all other content was produced.
As a final consideration, let’s briefly explore the time of day in which tweets are being authored. The following chart displays the number of tweets authored by hour and is standardized to UTC (London) time. In mentally adjusting the time, recall that London is 5 hours ahead of the East Coast (EST) and 8 hours ahead of the West Coast (PST).
For convenience, the same chart is duplicated below but framed in terms of U.S. East Coast time so that you it’s easier to think about it in terms of a continuous 24 hour period without having to “wraparound.”
There is clearly an ebb and flow of when tweets are authored with a spread that is well beyond twice the minimum value in the chart. It would appear that most of the tweeting is happening during and after the evening news in London and western Europe, which roughly corresponds to lunchtime across the United States. However, it does seem a bit surprising that there isn’t a similar spike in tweeting after the evening news in the United States.
With just a little bit of pre-planning, you can filter Twitter’s firehose for content pertaining to any topic that interests you and conduct a preliminary analysis just like this one and much more. A key part of making the whole process as easy as it should be is being equipped with the technical know-how and a toolbox that contains the right combination of templates. The GitHub repository for Mining the Social Web, 2nd Edition is jam-packed with useful starting points for mining Twitter as well as Facebook, LinkedIn, Github, and more.
Although, this post was just an exploratory effort that initially sized up a non-trivial data set involving more than one million tweets, we also learned a few things along the way and discovered a few anomalies that are worth further investigation. If nothing else, you hopefully enjoyed this content and now have some ideas as to how you could run your own data science experiment.
If you enjoyed this post that featured a sliver of what you can begin to do with Twitter data, you may also enjoy the broader story of social web mining as chronicled in a 400+ page book that’s designed to be the “premium support” for the open source project that’s on GitHub. You can purchase the DRM-free ebook directly from O’Reilly and receive free updates for life.
Read more about the journey of authoring Mining the Social Web, 2nd Edition and how I tried to apply lean practices to make it the best possible resource for mainstream data mining in Reflections on Authoring a Minimum Viable Book.