Update, Feb 2015: Tourists v locals: city heat maps showing geolocated tweets; tourists in CPH can be found in the city centre and at the airport, duh…but interesting concept! Here’s more…
- Locals and tourists 2015 – CPH (Twitter data; article | another) – what about Twitter usage culture osv?
- Geotaggers’ world atlas and Locals and tourists (Flickr and Picasa data)
- See something or say something (Flickr vs Twitter)
- Paths through cities (random trips based on random geotags)
- World languages mapped through Twitter
- locals vs tourists – how about internationals vs locals, or tourists…
- see also The geography of tweets
Cue #SoMe klaxon! Week 4 of #mapmooc looked at social media as spatial data, how social media can be used with maps, advantages and pitfalls…and just how easy it actually is to plot it on a map.
On Twitter few tweets are geotagged. We’re up to a grand total of three in the #mapmooc TAGS archive – two by me plus:
See the difference in @asudell‘s stream:
#vandymaps are also having issues:
Seems that tweets made with the web client only get geolocation information (coordinates) in TAGS if they are tagged individually, but not if the user has merely added location in Settings, which TAGS doesn’t collect (htow about the vanilla Twitter API?). OTOH mobile apps, with inbuilt GPS, _do_ offer geocoordinates simply when location is turned on. At least I think that’s right – thanks to @derekbruff and @asudell for sorting this out!
(Update: @derekbruff has set up a #vandymaps archive, and is investigating geotagging tweets. Checking the #mapsmooc archive reveals that two of my own tweets, where I added location via the Twitter Web client, are the only ones with data in the geo_coordinates field. I’ve extracted the data from the user_lang field and will take a closer look PDQ.)
But even a small set of tweets can offer potentially interesting results – see What’s happening in our vicinity from Field Office (an arts project currently going on in CPH) – a snapshot of geotagged tweets using the Streamd.in app, plus the Esri Public Information Map, in the week’s mapping assignment. This shows the real time effects of extreme weather events and other natural disasters, including geotagged social content from Twitter, Flickr, and YouTube. As noted in the forums however this is a rather blunt instrument with a poor signal to noise ratio.
Tweetmap Alpha is a further tool to filter geotagged tweets. As we know geotagging and privacy kinda go together. GeoSocial Footprint looks at the location information you divulge on Twitter in the light of potential privacy concerns. A footprint is made up of GPS enabled tweets, social check-ins, natural language location searching (geocoding) and profile harvesting. It states that “14 million tweets per day contain embedded GPS coordinates and up to 35% of all tweets containing additional location information”, which seems rather higher than in my experience.
Geolocating tweets the hard way
Back in lesson 1, it was noted that locations relevant to a particular tweet could include:
- the locations mentioned in the message itself
- the user’s location when they created the message
- the user’s home location
- the locations implied by the message
What are you plotting when you plot location? Where people live, where they work, where there is free wifi?
And from a thread, the following methods can be used to determine the spatial origin of tweets:
- gelocation (geotags?)
- Geo-IP and user designations (haven’t a clue)
- the location from the user’s profile
So, there’s more to it than geotagging via GPS. See for example Tweak The Tweet, which uses “a hashtag-based syntax to help direct Twitter communications for more efficient data extraction”.
A bunch of maps were presented on the forums, including a lone Facebook example (Mapping the world’s friendships), leading to extensive discussions on sentiment analysis and how it might/not work. Happy days!
For starters, at least three university projects use Twitter to understand [emotions] in the USA, including…
- Twitter can tell whether your community is happy or not – “examined 82 million tweets, mapped from nearly 1,300 US counties….each county had at least 30,000 twitter words geotagged to it” (paper presented at ICWSM 2013)
- The geography of happiness: connecting Twitter sentiment and expression, demographics and objective characteristics of place – see also Where is the happiest city in the USA | the Hedonometer | TEDx talk | Arxiv paper (2013)
- Twitter mood map reveals the world’s emotions (2011)
- Pulse of the Nation – US mood throughout the day inferred using the Google Maps API (2010)
- Twitter NYC – uses data from TrendMaps to show the language of tweets see Twitter Tongues for London
- new! EMOTIVE – project at Loughborough, analyses up to 2,000 tweets a second to extract from each a direct expression of one of eight basic emotions (Gdn story)
Other projects which may/not be connected to the above: Emography | Tweetfeel | Twittermood | We feel fine | Mappiness (UK). Enough already! Update, June 2014: Five Labs ” analyzes your Facebook posts to predict the personalities of you and your friends”.
More clues on sophisticated methods IRT geolocation no doubt to be found in:
- Leveraging geospatially-oriented social media communications in disaster response, plus SensePlace2 (Anthony and co @Penn State)
- Mining Twitter for airline consumer sentiment (inside-R)
- The Oscars and location based sentiment analysis via Twitter (Esri)
I could also do with:
- revisiting where week on #ivmooc
- delving further into the methodology behind Reading the riots and Mapping online publics
- trying out 16 sentiment analysis APIs and ANEW (Affective Norms for English Words)
A nice story to finish, in the warm up to week 5. #mapmoocer Tony Targonski created a map of Seattle on an earlier Coursera MOOC: “Larger circles mean more social activity. Greener colour represents more “positive” than expected; redder is less “positive” than expected. In this case “positive” refers to valence (a commonly used measure of sentiment), and “expected” is the predicted valence score based on the walkability measure of the block (overall more walkable places correlate with more positive sentiment).”
Which is an interesting point IRT Happy Denmark. They’re not happy, they just bike a lot (like I didn’t know).
#mapmooc statistics week 4 (7-13 August):
- 656 (558; 374; 206) tweets, 202 (181, 117, 82) RTs, 264 (212, 112, 61) links (all +/- due to time zone differences)
- top tweeters: @MapRevolution, @DougOfNashville, @PublicUniverse
- n=246 (230, 152, 129); 157 (155 (102, 74) have tweeted only once
- 61 (54, 40, 30) threads 9 (9 (11, 12)%
- top conversationalists: @MapRevolution, @derekbruff, @annindk
Postscript: among its rather nice web apps Esri offers a social media app (hopefully a bit more stable than the gallery app) plus stuff on making a social media map in minutes – come in! See the Horn of Africa Drought Crisis Map for an example.
As a quick test I took a look at Denmark’s most popular hashtag,#dkpol. Danes aren’t big tweeters, but they are big mobile users and #dkpol people are a pretty vociferous bunch, but the results were rather underwhelming. Putting #SoMe on a map seems to be less about creating a meaningful map and more about simply harvesting the data – see We are on Albert Drive for an example of what can be done. To be revisited.