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Tag "twitter"

The Olympics are in town and about to kick off tonight in a packed Olympic Stadium out in Stratford. The last week was all about gearing up to for London to this big event. There were a few new changes, including the Olympic lanes for official traffic, but also simple things like chaining the timing of traffic lights for example.

Image taken from zimbio / The Olympic Rings 2012 being shipped up the Thames past the O2.

Image taken from msn.car / The official Olympics 2012 London car.

However so far things are running smoothly if only the weather plays along. But then a bit of the very British weather won’t harm the good spirit, it’s the Olympics!

The venues are reported to be all set. The velodrome was one of the first venues to be finished already last year. Now the Olympic Stadium is open, the Aquatics centre plus the little venues. Also the observation tower in the Olympic Park is open to visitors, at extra cot unfortunately.

Image taken from London2012 / The Olympic Park as of July 2012. Compare to earlier stages for example in previous posts on urbanTick.

London has prepared through out the city a massive events program to go alongside the Olympic Games. There are cultural events like the Tate is running at the newly opened Tanks or of course the official Olympic Festival with a massive program of arts and culture events through out the Olympics.

The sponsors have all their own way of being present at the games. Coke has set up a pavilion that is at the same time a musical instrument. The facade is built from sensor equipped cushions and visitors can play tunes by interacting with the facade of the pavilion.

EDF, also one of the big sponsors is running a special light show on their very own London Eye. Every evening the light on this big London attraction will have a light show on display that is governed by the mood of the nation.

Image taken from gizmag / The London Eye with the Energy of the Nation light show in progress, earlier this week.

The installation is using Twitter data to feel the pulse of the nation through out the day and summarise it in the evening for a show of flashing lights and colours. The data from Twitter is analysed regarding the positive or negative content of the message. The overall count of this rating is then via an algorithm transformed into the pattern of light and colour displayed on the wheel.

For the Energy of the Nation project, EDF is work with Mike Thelwall, from the University of Wolverhampton and SOSO design company on this project, to light up the London Eye with a daily custom light show.

Talking about Twitter data visualisation another one, pretty unrelated to the Olympics has been put together recently by Nikhil Bobb. Its a lens flare sort of visual effect to let the tweets blink up on a map. Looks very nice and the map is interactive and you don’t have to wait until the evening to enjoy it. You can check it out round the clock fro London from HERE. Other cities are in the list on the left if you want to travel the world on a lens flare trip. Via Living Geography.

Image by urbanTick / Tweet flare visualisation of real time tweets by Nikhil Bobb.

Let the Games Begin!

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The advances in online data mining and the rising popularity of online social networking data is posing challenging questions in regards to ethics and privacy. How can academic research provide a comprehensive framework to secure data management and guarantee appropriate handling?

Given the current popularity of data crunching, big data and visualisation of massive datasets the question of data management under ethical guidelines in a lot of cases are pressing. Current institutional protocols do not cover these new aspects that arise from the accessibility of large datasets of online data.

Social science so far still builds on the basics of informed consent with all involved participants. These protocols were implemented in the late seventies, long before the internet. Most of the protocols have been updated around the year 2000 in regards to online research involving online questionnaires and sometimes research with chat rooms.

The dramatic changes online social networking data brought along with API’s allowing the construction of large scale datasets connecting to Facebook, Twitter, Foursquare and the like are based on the multiplication of dimensions. Researchers are no longer working with 10, 100 or 1000 participants, but potentially with data relating to millions of individual users. Still the data in as detailed as a qualitative dataset with 100 participants might be, potentially in specific cases even more detailed. This is especially the case in regards to time and location.

Currently the discussion mainly circles around the question whether the data is free and publicly available implying that if it is to be considered so no additional measures would be necessary. The argument in this case would be that the individual users are voluntarily sharing the data publicly for free. This is however a very naive and short sighted argument. There are of course a number of complicating issues to be considered. There are three main elements to this.

NCL Twitter Sheet
Image by urbanTick for NCL / A screenshot of a Twitter data table with the different columns containing metadata. Each row represents one tweet.

The first aspect is the dynamic nature of the data. Since the data is time based and it is being produced at such a vast quantity content very quickly is superseded and disappears in the platform’s thumbs in many cases unretrievable for the individual user. In practice this can result in the fact that sets of mined data are becoming unique. In this case the acquiring of such a dataset is an act of making for which the research would have to take responsibility.

The second aspect is that the service operational aspects. It requires the user to share the information as otherwise the usage of the service in most cases would simply be impossible. If the user would not be willing to share the information this would in most cases result in the exclusion of the user or at least mean a dramatic reduction of the capacity of the service. Another aspect of the usability is that the way the user interacts with the platform easily can lead the user to believe to be acting in a private environment. In the individual setting the service only provides information of a closed circle of connections to other users. This means that the users might be tempted to share private information easily not being aware that on a larger scale all activities are public. Furthermore, it is unclear if the user has, by agreeing to use the service also agreed for all his information to be mined and researched towards specific conditions in relation to a vast number of other users.

The third aspect is the fact that no the individual datapoint, message or information is causing concern for privacy, but the series of datapoints. These newly available datasources contain a lot of metadata and continuous data which has the potential to be analysed towards patterns. In other words it is not about one or two places the individual has been to, but about the possibility to infer a very personal pattern from the information distinctively describing the personal habits in both time and space.

From these considerations and points of discussion the now published paper Agile Ethics for Massified Research and Visualization as part of the special edition of Information, Communication and Society, edited by A. Carusi is available online from Taylor & Francis.

The paper is written together with Dr. Tim Webmoore at Stanford and beside the discussion of implications as well as aspects of the development of a framework the Twitter work serves as a practical example.

The topic has already been discussed in an earlier blog post Privacy – Aspects of an Ecology of Ownership that lead at a later stage to the paper. Also a version of the paper has been presented at the Visualisation in the Age of Computerisation conference in Oxford in early 2011.

Neuhaus, F. & Webmoor, T., 2011. Agile Ethics for Massified Research and Visualization. Information, Communication & Society, pp.1-23.

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Today is the second day of the Second International Conference of Young Urban Researchers in Lisbon at ISCTE-IUL.

The conference aims to share recent researches on urban contexts from many different areas of social sciences, to discuss current theoretical and methodological issues and to promote interdisciplinary and international networking. It is intended that the meeting should be boosted by young researchers who work in urban studies and develop research in the cities – especially those who are studying in post-graduate programs but also those carrying out technical and intervention activities.

SicyURB Lisbon coference poster
Image taken from SicyURB / conference poster.

My contribution with the title Location Based Social Networks and the Emerging Sense of Place will be focusing ont he emerging potential of social media data to chalenge and redefine the established cartesian cartographies of cities by generating its own detailed descriptions of spaces. These spaces are temporal, ephemeral in nature making them hard to grasp and categories in a conventional way.

The conception of identity in this case is less the idea of the individual perception of spaces and the creation of a personal tie than it is a collective description of an emerging spatial identity as a description of spatial activity defining the urban space. Identity would here be the spatial description as such, making use of different aspects, including time, space and social connections.

The talk will be based on the assumption of a departure from the static urban conception as a given framework towards a much mor specific, individual and timed conception of city in the context of the now widely available tools and data sources. This includes a number of urban sensors providing real time and very contextual data. This can be local sensors but also includes the citizens themselves as sensors through mobile technology and social network media. With this information that is no longer gathered under the objectivity dogma, no longer serves to support the city as an institution but is highly situative and subjective to the degree that it is potentially not repeatable definitely not in a different context.

At the same time these new datasets also chalenge the established data sources on the level of quantity. So far research into the field of spatial description challenging the established objectivity were doomed due to their qualitative nature based on small ‘none’ representative samples and methods of data collection. However, the emerging data sets, provided by urban sensors, are available in numbers outshining many of the conventional quantitative sources. Therefor the argument of representativity does not bite no longer and visualisations and research is fast tracked into the interest focus.

This is not without problems of course and the description and relations of the available data sets is still vague and laks clear handles and definitions. Similar it is the case with ethical and regulative questions especially regarding responsibility and accountability. So far the institutions have not picked up on the problem and existing ethical protocols do not yet include the new questions of ownership, security and management.

Using the social networking data it might become possible to depart from the starting point of time geography by implementing the described dynamics on the level of data and start stitching together a picture of the urban environment more in the sense of Guy Debord’s naked city proposition that proposed a mapping based on experience.

However, the use of these new data sources is still at the very beginning and specific strands of interest are only beginning to emerge. The New City Landscapes are a start trying to visualise the different characteristics on a city level.

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Twitter data is becoming a new rawmaterial for representing cities. Visualisations are being produced frequently. The latest addition comes from Trendsmap the online platform visualising emerging Twitter trends.

The guys have produced visualisations for a number of cities from around the world plotting locations of georeferenced tweets. The series is called Paint a City by Numbers and so far covers only a doyen places, but is poised to grow with Trendsmap having access to a lot of Twitter data through heir service.

Trendsmap painting cities, Melburne
Image taken from trendsmap / Painting the city of Melburne using geolocated tweets.

Trendsmap painting cities, Sydney
Image taken from trendsmap / Painting the city of Sydney using geolocated tweets.

These sort of maps we have seen already for examples in the work of Eric Fischer. Still it is always amazing as to how much detail the maps actually contain with streets completely covered. For example in the map painted of the area around Amsterdam in this example HERE, the main roads draw out in amazing detail.

However Trensdmap have added also specific features. One of the fascinating ones is the airport. Here on urbanTick we have pointed out a number of times how different urban features draw out specifically in the city fabric and the airports are definitely a special case.

The Trendsmap guys have plotted data for the area around the Atlanta International Airport and the resulting creepy crawly bug structure is amazing.

Trendsmap painting airports, ATL
Image taken from trendsmap / Redrawing the airport of Atlanta ATL, actually the busiest airport in the world in 2011.

Trendsmap painting airports, SFO
Image taken from trendsmap / Redrawing the international airport of San Francisco resembling the shape of a spider using geolocated Tweets.

Via GoogleEarthBlog.com

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Singapore is an city state with about 5’076’700 inhabitant according to the 2010 census. The society is very technology interested and electronics make a lot of their business.

Digital elements have a strong presence in everyday life, including online social networking. In this sense it is not surprising that Twitter is very popular in Singapore. Also in terms of location sharing, users in Singapore are quite happy to share their location with the tweets. We have about 46% of location based messages. This is only matched by Amsterdam, NL and Lagos, NG. The average over all the locations observed is about 10-12%.

Singapore New City Landscape
Image by urbanTick for NCL / Singapore New City Landscape map generated from location based tweets collected over the period of one week. The area covered is within a 30 km radius of Singapore.

The virtual landscape redraws nicely the outline of the island state. There are the neighboring areas of Malaysia and Indonesia showing up at the top and the bottom respectively of the map. The connection across the water are also showing with tweets send either from a ferry crossing or from one of the two bridges. Beside these connection the international airport on the far most East corner of the island is probably even more important to connect to the outside. It features prominently in the landscape as a tall peak of high tweet activity.

Singapore New City Landscape

Image by urbanTick using the GMap Image Cutter / Singapore New City Landscape Use the Google Maps style zoom function in the top right corner to zoom into the map and explore it in detail. Explore areas you know close up and find new locations you have never heard of. Click HERE for a full screen view. The maps were created using our CASA Tweet-O-Meter, in association with DigitalUrban and coded by Steven Gray, this New City Landscape represents location based twitter activity. Thanks for the naming help to Kai from 3rdlifekaidie.

Overall the virtual landscape beautifully redraws the outline of the island Singapore is on. The main features that immediately stand out in the Singapore NCL map are the areas message are absent. The large Nature Reserve in the centre is the larges area with reduced Twitter activity, but also the live firing area and reserve on the western side of the island. In line with the other observed urban areas, outdoor spaces show lower Twitter activity.

On the other hand the complete south coast of the island is abuzz with activity. Ranging from Changi International Airport in the East all the way past the container ports to the West tot he industrial areas. The main peak is Dhoby Gaut Peak in the area of the major interchange station on the MRT.

Singapore timeRose
Image by urbanTick for NCL / The rose shows the twitter activity per hour of the day, starting at 00:00 at the top, displayed as local time. Singapore is an evening city with a clear activity peak between 21h00 and 23h00. Mornings are very slow and it doesn’t really pick up until the late afternoon. The graphs show the platform of preference used to send the tweet and the language set respectively.

The languages represented in the Singapore data set are clearly dominated by English and Indonesian. Those two languages cover about 90% of all messages. Interestingly the other few languages featuring are European rather than Asian. There is Dutch, Norwegian, Italian and Spanish, German and French. Also Esperanto again features, though only with for marginal number of tweets.

The platform is dominated by twitter for iPhone, followed by twitter for Blackberry, the web and tweetDeck. The iPhone seems very popular in Singapore. Also the iPad features on the tenth rank with twitter for iPad.

The temporal structure of Twitter activity is extremely focused on the evening. No other city has such a strong activity preference as Singapore shows. There is a clear peak between 9pm and 11pm. with a extreme drop of after 1am to nearly zero activity between 4am and 5am. This is followed by a sharp start in the morning around 8am. Through out the day this stays about leveled until it starts to rise in the late afternoon after 6pm.

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London was the first city we collected Twitter data for when we started to create the New City Landscape (NCL) project, monitoring location based Twitter activity in urban areas. This was back in May 2010 and since we have collected data for a lot more cities from around the world.

We have now finally also an animated NCL (aNCL) version using the same dataset. This part of the project was only developed earlier this year in collaboration with Anders Johansson at CASA and we are trying to catch up on the different cities we have data for. A series of aNCL visualisations has already been realised.

aNCL London
Image by urbanTick for NCL / Showing four screenshots taken from the aNCL visualisation for a weeks worth of Tweets in and around London. The timings are midnight, morning, afternoon and evening. Each do is a tweet, re-tweets show a lin between sender and re-sender.

There are only very few features we are using for these visualisations. A characteristic landscape feature to roughly describe the urban area and the 30 km collection radius parameter to provide scale. Other than that there are only the individual Twitter messages that were collected over the period of one week. THe animation superimposes all seven days in to 24 hours.

With the visualisation we are highlighting the way information disseminates through re-tweeting of messages. An RT message will show a thin yellow line between original sender and re-sender. The information travels at some speed, which is based on the time it takes between sending and resending.

London, even though the data is already a year old is compared to other cities a very busy place in Twitter terms. We have a lot of individual messages, but more interesting there are quite a lot of different interactions happening simultaneously. Where as other cities don’t show a lot of interaction, in London the sharing of information is quite an important part of tweeting. An interactive, but static activity map can be found at London NCL.

Its great to see how London wakes up between 07h30 and 09h00 in the morning after a moderat night. Then there is however, not very much sharing at this point of the day. Only after lunch and especially later in the afternoon the sharing really starts in London. It is almost as if the city was to digest the information it had created earlier in the day, reprocessing it and passing it on.

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Barcelona is in terms of twitter activity one of the cities that has a strong central core of high activity. Very similar to for example the London NCL or the Paris NCL maps.
The highest point is just over the Placa de Catalonia with a steep slope down la Rambla to the Roca Columbus. Other places of high activity are around the parliament, here the ‘Monte di Parliament Catalonia’ and around the Olympic centre on Montjuic.

Barcelona New City Landscape
Image by urbanTick for NCL / Barcelona New City Landscape map generated from location based tweets collected over the period of one week. The area covered is within a 30 km radius of Barcelona.

The Barcelona New City Landscape map has already been published earlier, but it needed an update because of some problems in the processing and labeling. This new version also goes in line with the adjusted layout and design.

Thanks for the help with the map go to Narcis Sastre, who kindly worked it through.

Barcelona New City Landscape

Image by urbanTick using the GMap Image Cutter / Barcelona New City Landscape Use the Google Maps style zoom function in the top right corner to zoom into the map and explore it in detail. Explore areas you know close up and find new locations you have never heard of. Click HERE for a full screen view. The maps were created using our CASA Tweet-O-Meter, in association with DigitalUrban and coded by Steven Gray, this New City Landscape represents location based twitter activity.

Barcelona is very active in the afternoon hours. There is a peak around 15h00, 18h00 and 21h00, after which it quickly drops off. The mornings are very pronounced right after six, however overall far less than the afternoon. Over lunch there is clearly a dip with lesser activity.

Spanish is clearly the dominating language, followed by English. Indonesian, French, Portuguese and Italian are sort of the runner ups. ALso Esperanto is there, this is surprisingly often present in the top ten list and it seems that a lot of people are using it as a statement, since it is not really a spoken language.

Barcelona timeRose
Image by urbanTick for NCL / The rose shows the twitter activity per hour of the day, starting at 00:00 at the top, displayed as local time. Barcelona is a afternoon city with more activity between three and nine than through out the rest of the day. The graphs show the platform of preference used to send the tweet and the language set respectively.

Also, we have the animation ready for the Barcelona data set. This one is put together in collaboration with Anders Johanson. The animation also shows the interaction between the users based on RT and @ tweets with thin yellow lines. This indicates a direction and provides a sense for the distribution of flows.

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Calgary was established in 1875 as Fort Brisebois by the North-West Mounted Police, located at the confluence of the Bow and Elbow rivers in what is now Calgary, Alberta. Today Calgary is the largest city in Alberta and directs an oil and gas empire, making a rather good situated urban centre.

The city is located similar to Denver at the transition zone between the prairie in the East and the Rocky Mountains in the West. This dominates the views and the impression of the place. However, unlike in Denver the tall mountains are further in the distance. The rockies in Alberta fade out in to the flat land with smaller hills.

Calgary New City Landscape
Image by urbanTick for NCL / Calgary New City Landscape map generated from location based tweets collected over the period of one week. The area covered is within a 30 km radius of Calgary.

The urban area is quite active on Twitter and the NCL map draws out the city features nicely. The main features are the airport, the downtown, the absent Nose Hill Park. Also the the main movement corridor the famous Calgary Y shape shows up on the map as a North West to South Centre connection with a North East connection via the airport.

Calgary New City Landscape

Image by urbanTick using the GMap Image Cutter / Calgary New City Landscape Use the Google Maps style zoom function in the top right corner to zoom into the map and explore it in detail. Explore areas you know close up and find new locations you have never heard of. Click HERE for a full screen view. The maps were created using our CASA Tweet-O-Meter, in association with DigitalUrban and coded by Steven Gray, this New City Landscape represents location based twitter activity.

The main peak is over the downtown area as expected. The summit is right above the Scotia Centre building. This central mountain extends to the South West towards the Access Cliff and to the North East to the Highland Park Cliff.

Some of the missing areas are the Nose Hill Park which appears completely empty on the Twitter map with basically no activity. Also around the CPR the tweet desert spreads far, all the way down to the Bennett Forest.

Calgary ca 1885
Image taken from Wikimedia / Calgary as it appeared circa 1885.

The Y shaped sequence of hills connecting the downtown area to the outskirts follows the main transport arteries of Calgary, the C-Train.
This transport system is the core of public transport in Calgary and free in the Downtown area but extends beyond. It is accomplished by a bus network.

The City of Calgary is very much a morning city with quite a bit more tweets sent in the morning hours between eight and twelve. The afternoon is slow and the nights early, with the characteristic night dip starting before three and ending soon after four.

The dominating language is clearly English with other languages used on Twitter in this area being very low. For the platforms used the four dominating ones are Twitter for iPhone, Ueber Social, Twitter for Black Berry and the web.

CalgaryNCL timeRose
Image by urbanTick for NCL / The rose shows the twitter activity per hour of the day, starting at 00:00 at the top, displayed as local time. Calgary is a morning city with more activity between eight and twelve than through out the afternoon and evening. The graphs show the platform of preference used to send the tweet and the language set respectively.

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I am at CUPUM presenting a paper on the ongoing New City Landscape location based Twitter message mapping project. The paper gives an overview of a whole range of aspects this project is working with. Rangin from data collection, ethical discussion of ‘tweets as public data’, mapping the virtual landscapes, to temporal aspects.

Lake Louise Postcard
Image taken form Attic Postcards / Vintage postcard, dating ‘White Border’ 1915 – 1930, showing the Chateau Lake Louise on the shores of Lake Louise in Alberta, Canada.

The presentation also gives a brief outlook of what the upcoming directions for the project could be. Earlier there were a number of these directions presented on urbanTick. This ranges from crowd sourcing to the tracking of public events or disaster as well as networking, connecting social and spatial aspects.

Some of these are currently being details and for other development of methods is under way. Recently the networking aspects were in the focus. There were network maps based on the London data, but also the Munich data and the San Francisco data. Most recently there were some visuals looking at intercity relationships using a data set that contains tweets for Switzerland were the alps as physical barriers showed up clearly in the network.

The presentation can be found below or on Prezi.org.

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How does the social network link location as people communicate? It would be very interesting to see how communication pattern link to location and context.

There is already quite some good stuff on this topic. Only recently John Reads defended successfully his PhD on a topic in this field. He was looking at telecommunication patterns in the South East and London region base on land line connections. He found some interesting patterns of linkages and hubs and was able to identify regions according to dominant trades. Also some landscape features showed up. For example the Thames was acting as a barrier even in the realm of phone call connections.

The TwitterNetworks of London, San Francisco or Munich generated from the NCL datasets are quite interesting where we can see how individuals are connected via interaction on te Twitter plattform using @-tweets and RT-tweets. The networks are built establishing the edges using these two direction indicators. Other networks based on twitter data have been focusing on institutions and text as with the ‘Why Mediate Art‘ project.

Image by urbanTick for NCLn / Circular graph showing the connection between the data sets collected for different urban areas in Switzerland. No connections between them exist, each one operates separate.

Recently the NCL network ha been looking at areas in Switzerland and mapped out the four large urban areas Zuerich, Geneva, Basel and Bern. Overall it is quite visible that Twitter is not as popular as it is in other parts of the world. It is still sort of seen as a time waister and something for nerds. This was aso reflected in the language settings with foreign languages dominating the fields. It seems quite popular with people to keep in touch with other parts of the world.

Switzerland is quite small and the linkages between the cities are well established. This is supported by a perfect public transport network and a wealth of political mechanisms to ensure equality and exchange. However socially of course the different languages to separate different communities. French is spoken in the West, German in central and North and Italian in the South of Switzerland. In the middle of sorts there is the dominating geographical landscape feature, the alps acting as barriers.

This complicated setting of barriers and ties makes Switzerland an interesting study object for social networks. It would be great to see how the different urban areas connect via individuals on social networking platforms.

Regarding our Twitter data we have two data sources. One is the individual records for each urban area over the period of one week, the other is a week long record of location based tweets sent across the whole of Switzerland including the south of Germany and the north of Italy with Milan.

Image by urbanTick for NCLn / Showing the social connections as found on Twitter within Switzerland. Data based on a location based Twitter record over the period of one week.
The alps in the centre act as barrier with only a few connections crossing them, either via a celebrity Twitter account, in this case Justin Biber and Jessica Alba or physical travel.

Looking at the first data set with individual records for each urban area, the networks separate out each city with no established connections between them. It was sort of expected, since the sample is small and the parameters are tight with only location based tweets.

Looking at the second dataset where the whole area was simultaneously recorded the big barriers show up clearly and there is a well established separation between North and South showing the alps as mountainous Twitter blockers. Interesting however, are especially the links across this barrier and there are some. Three links connect the norther part consisting of Geneva, Basel, Bern, Zuerich and southern Germany with the southern part consisting of Milan, Como and Turin. They represent different types.

The first type is the link via a common third party that is not necessarily in the area. This is most likely a very active and popular twitter acount. In this case there are three celebrities that establish the connections. They are Justin Biber, Jessica Alba and some guy Kenny Hamilton. These are the hubs that individual Twitter users from both sides of the mountains tweet at and establish a sort of second grade connection.

There is also the other type of first hand connections. this is established by an individua traveling around and tweeting both to connections in Milan, the southern part and users in Zuerich, the northern part. The fact that the individual has actually physically traveled between the two parts enforces these connections.

Image by urbanTick for NCLn / Switzerland with the locations of recorded tweets. This has been collected over a period of one week. The purple line shows the route travels by one individual users tweeting along the way and interacting with other user from both sides of the mountainous barrier.

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