Black cabs, Brexiters, cyclists: a battle for London’s streets on Twitter

Are black cabbies the best lobbyists in the LTN debate? Perhaps. This is the second in a series of three posts present data analysis of the Twitter activity around London’s contentious Low Traffic Neighbourhoods.

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An LTN in Oval, photo from

This is the second in a series of three posts analysing the Twitter debate around LTNs, using data from the Twitter API. It looks at how black taxi drivers play a special role in the Twitter LTN debate. My first post gives some background and reflects on the fact a handful of hyperactive individuals account for much of the LTN activity on Twitter. My third post outlines some speculative ideas that might improve the situation.

My goal is not to participate in the partisan ping-pong of Twitter, but I can see that the topic of Black Taxis, let alone Brexit, are perspective that some might find provocative.

Summary of how the data was collected

The first post describes my method in detail. In brief, from October 1st, all tweets were collected from 130 Twitter accounts most actively using the #LTN hashtag are related terms. The dataset includes accounts mentioned by and followed by the initial group of 130 accounts — the total dataset contains around 91,000 accounts.

  • The 130 most active accounts were categorised as Pro- or Anti-LTN, by looking through their timeline for at least two tweets voicing strong views. 66 were anti-LTN accounts, 55 were pro-LTN, 9 were ambiguous.
  • Users’ Twitter biographies were used to label accounts as follows:
    — ‘black taxi driver’ specifically identified themselves in their bio
    — ‘cyclist’ if their only or primary interest in their bio was cycling~
    — ‘pro-Brexit’ or ‘anti-Brexit’ if the bio unambiguously stated a view

I saw no evidence of Uber of minicab drivers.

Black cabs are a distinctive community.

In the diagrams below, the coloured circles represent Twitter accounts; the lines between them represent a connection. The connections shown in the first two diagrams are @mentioning and retweeting. In the third diagram, the connections are ‘following on Twitter’. The diagrams are laid out so that Twitter accounts that have a lot of connections placed close together, those with fewer are further apart. The size of the coloured circles represents the number of connections they have.

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Figure 1 — Twitter retweet and @mentioing interactions

Figure 1 shows the network of Twitter interactions. Black taxis have distinct characteristics, showing a tendency not to message pro-LTN accounts, choosing to oppose LTNs from deep within the ‘anti’ community. In the online debate, cyclists are often seen as the most ardent supporters of LTNs. Unlike black taxis, they do not appear separate from the wider pro-LTN network.

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Figure 2 — Twitter retweet and @mentioing interactions, with algorithmic community detection

Figure 2 shows the same Twitter accounts as above. An algorithm (modularity) has been used to detect communities — that is subcommunities with a higher link density than the network as a whole. A group that corresponds with black taxis is detected as a separate community. The algorithm further detects two geographic communities, Ealing and Lewisham (containing both pro- and anti- accounts).

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Figure 3 — Twitter ‘following’ behaviour

Figure 3 shows Pro- and Anti-LTN users clear form distinctive groups. Within Anti-LTN campaigns, black taxi drivers come out as a distinctive community, both in terms of connections and the types of accounts they follow: black taxi Twitter accounts have a strong tendency to follow pro-Brexit accounts, Pro-LTN accounts have a weaker tendency to follow anti-Brexit accounts. By comparison, Non-taxi Anti-LTN accounts rarely follow Brexit aligned accounts.

What do these diagrams indicate? That black taxis form their own Twitter community, are represented disproportionately in the debate, and are not politically typical of anti-LTN campaigners.

Black taxis as a community
In Figure 1, we can see that cabbies are often located close to one another in the network, but perhaps not in a particularly distinctive way — except that they cluster furthest away from the pro-LTN accounts. However, the modularity algorithm used in Figure 2 underlines the point that cabbies are a separate community — they form a discrete natural grouping because of the density of connections between them. Finally, in Figure 3, we can visually see how accounts belonging to cabbies are clustered (the modularity algorithm again detects the cabby community, though I haven’t included a diagram). These features are strong evidence for a separate black taxi community.

Often, LTN debate is caricatured as ‘cabs vs cyclists’. I tried to find a sub-community of cyclists within the pro-LTN movement, but they are not clustered in the network in the way cabbies are. Further, except for two professional bike mechanics, cyclists do not typically earn a living from cycling, or professionally identify as cyclists. In that sense, ‘cyclist’ is a weaker category than ‘black taxi driver’.

Black taxis and representation
London is a city of 8 million people with around 20,000 registered black taxi drivers, so cabbies represent less than 0.25% of the population. In my dataset, black cab drivers account for around 8% of the 120 most active accounts. Black cab drivers are therefore disproportionately represented.

Internationally, taxis have often acted as a special interest group that has been extremely effective at lobbying for transport policy for their own advantage (a phenomenon that some economists consider predictable). The disproportionately large and distinctive taxi community on Twitter suggests the same type of lobbying campaign may be underway in the case of LTNs.

Black taxis and politics
I am not making a case that supporting or opposing Brexit is problematic or relevant to transport policy, except to the extent that it marks out distinctive subcommunities and suggests how views on LTNs connect with wider political views.

The patterns in the way that accounts engaging in the LTN debate follow Brexit-related accounts are likely to be at least partly demographic — e.g. taxi drivers might be older and older people tend to be more pro-Brexit, while anti-LTN individuals are younger, so demographically tend to be anti-Brexit. It’s less obvious how the discrepancy between taxi-drivers and non-taxi anti-LTN Twitter users can be accounted for. Most people who have been in a black cab will not be surprised that cabbies are often pro-Brexit, but the fact that non-taxi anti-LTN campaigners are not Brexit-aligned might be more surprising.

Corroborating the evidence from the network data, a former UKIP parliamentary candidate, David Kurten, has spoken at anti-LTN protests, including at two organised in predominantly by cab drivers. Nigel Farage, the former leader of UKIP and driving force behind Brexit, has recently announced his formation of an anti-bike lane political party — presumably in the belief that his base will connect with the issue.

If black taxi drivers are bringing Brexit polarisation to the Anti-LTN debate, it may be a disservice to non-taxi anti-LTN campaigners who wish to make their case on the merits, rather than culture or ideology. Anecdotally, Nigel Farage’s anti-cycle lane political party has been divisive in the Anti-LTN community.

What does the LTN debate teach us about Twitter?

Tweets have so little social context. Is the author local? How long have they been local? Do they use cycle lanes or roads professionally? Even if a person’s tweets give a rounded picture of them as a person, the retweet mechanism can take a single tweet completely out of context.

Black taxi drivers are a particular case, more generally, the fragmentary nature of Twitter disorients us and makes it hard to understand the basis on which an individual asserts their right to pass comment on a particular issue. Sadly, it’s easy to assume the worst about the motivations of others.

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Tweet from the Mayor of Hackney, Philip Glanville. Much of the debate has been concerned with the idea that people from outside the legitimate community are imposing their views.

The first two posts uncovered patterns in the LTN debate. What if we could surface these types of patterns in more Twitter debates? Would civic discussion benefit? My third post addresses this question.

Third post: speculative social media tools to improve civic engagement →

PhD on digital systems for collective action and social network analysis.

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