Rules matter: why the current Labour crisis is not (only) about ideology

This piece was originally posted in the Constitution Unit’s blog

The Labour Party’s current crisis is often characterised as an ideological dispute between the Parliamentary Labour Party and a membership that is significantly more left-wing. But, as I demonstrate here, it is hard to stand this up. The ideological distance between Labour members and MPs is in fact smaller than that between Conservative members and MPs. To explain why many are now suggesting that Labour is on the verge of splitting it is necessary to look at party rules as well as ideology.

The situation within the Labour Party has been described by many as a dispute between the Parliamentary Labour Party (PLP) and the membership. The en masse resignations from the shadow cabinet, followed by a vote of no confidence from 81 per cent of MPs, shows that Jeremy Corbyn has lost the trust of his peers (or perhaps he never really managed to obtain it in the first place). Labour activists, particularly those grouped around the Corbyn-supporting Momentum, accuse the PLP of betraying the party and lining up with the right-wing. On the other hand, MPs respond by pointing out that voters, and not members, elected them and that they have a mandate to protect the party from oblivion.

Regardless of how relevant it might seem under the current situation, the ideological distance between members and party elites is not a new interest for political scientists. John May’s curvilinear disparity law explains that more active members are usually more ideologically extreme than MPs and voters. As Meg Russell states in her book Building New Labour, there is a limit to how much a leader (or in this case party elites) can steer a party’s position to the left or right. Therefore, we could expect that a widening gap between members and the MPs may result in a difficult situation for the party, or even an eventual split. With that in mind, I set out to investigate – in a very preliminary way – if this ideological gap can explain Labour’s crisis, and if not, what are the alternative theories.

To measure the ideological position of the different groups, survey instruments usually carry a question asking respondents on where they see themselves in a left to right scale. These measures take values from 0 for left to 10 for right. In order to estimate the average position of party voters, I use the British Election Study post-election face-to-face survey. For the ideological position of the MPs I use the data from Parliamentary Candidates UK’s Representative Audit of Britain project, which surveyed candidates from the last general election. The estimates for party members and supporters were obtained from the Party Members Project (PMP).

In order to assess how unusual the ideological distance between the different groups of the Labour Party is, we can compare it to what happens on the other side of the chamber. May’s law is not circumscribed to a particular ideological position, so it should apply to parties of the left and the right. The figure below shows the ideological position on the left-right scale for every group in the Labour and Conservative parties. As expected, members and activists appear as the most extreme in both parties, whereas voters tend to position themselves closer to the centre. What is striking is that the absolute distance between members and MPs seems higher in the Conservative party (0.9 points) than in the Labour Party (0.6). Not surprisingly, Tory MPs appear to be closer to their voters than Labour MPs.

Ideological positions for each group (dotted lines represent the party average across all groups)
Ideological positions for each group (dotted lines represent the party average across all groups)

We can conclude two things from this graph. The first is that May’s law seems to apply to the current situation in both major parties in the UK. The second is that reducing the current struggle in the Labour Party to an ideological distance between the members and the PLP does not stand up in the light of evidence. If anything, the distance between members and MPs should produce more problems for the Conservatives than Labour. The Tories are indeed in the middle of a difficult process for electing their new leader, but few have seriously raised the possibility of a split.

Obviously, a left to right scale might not capture other dimensions of the ideological divide. The Guardian columnist Owen Jones has argued that the big red line among Labour Party members for electing a new leader is the candidates’ position on the war in Iraq – a prominent issue with only a few days until the release of the Chilcot report – rather than who is more left-wing. However, that argument implies that most politics within Labour can be explained by a single issue, which seems unlikely.

If ideology is not enough to explain the current crisis, then what is? I offer three complementary explanations:

  1. In January, Meg Russell pointed out how Labour MPs have failed to understand their new role in the leader selection process. Following the rule changes introduced under Ed Miliband, MPs no longer have any more voting power than individual members, registered supporters and affiliated supporters. Rather, their role is similar to the one that Conservative MPs have in their leadership contents: gatekeepers. MPs are responsible for putting forward candidates that will not jeopardise the party, both electorally and organisationally. By letting Corbyn – who represents the most left-wing position amongst MPs – run for leader, they failed in their job to act as gatekeepers, and behaved as facilitators instead.
  2. The rules for selecting the leader allow for ideological polarisation by simply stating a threshold of supporting MPs (and MEPs) rather than a fixed number of candidates. Conservative Party rules require that MPs go through successive ballots until they submit only two candidates to the membership. Conversely, the Labour Party only required 35 signatures from MPs or MEPs to run for leader when there is a vacancy. This means that, theoretically, the last election could have had up to six different candidates (it had four). Instead of forcing an election between candidates who reach to all sections of the party (voters, supporters, members, and elites), the rules allowed for more polarisation which, as we see above, favoured the position of members over the rest.
  3. Finally, under the current rules, the leader of the Labour Party is not accountable to the PLP. This might seem obvious given that those voting for the leader are the members and supporters, not the MPs. However, the current situation shows how important it is that the leader is able to bring together all groups within the party. As we have seen earlier this week, not even a vote of no-confidence with over 80 per cent support can force Corbyn to resign, nor stop him from putting his name forward in the event of a challenge of his leadership. Under the current situation, is difficult to see how the Labour Party can provide a strong opposition to the government, if any at all.

The main take home point from this exercise is that the Labour crisis is not (only) ideological. Preference heterogeneity within parties is a well-documented phenomenon and the internal mechanisms should be able to cope with it. This is exactly what has been failing in the case of Labour. MPs were not able to understand their role as gatekeepers and guardians of party unity, but also the rules do not give them enough power to fix the problem. It seems that in this case, the leader of the party is willing to test how much he can steer the party to the left before breaking it.

Thanks to Professor Paul Webb for sharing the descriptive data from the Party Members Project.

The online world replicates traditional offline structures and networks of social capital.

This piece originally appeared on USAPP- American Politics and Policy.

Are new technologies changing the way in which we conceptualise and practise politics? This is the fundamental question to which we investigate in new research. In particular, we are interested in understanding how social capital is structured in online networks. Our findings show that the traditional structures we observe in offline settings, such as traditional social movements, organisations or political parties, are replicated in the online world. Moreover, supporting others’ findings from we claim that traditional behaviour continues to play a key role in the way in which social networks are structured.

We follow the traditional approach to social capital devised by Robert Putnam, understanding it as the presence of social networks that are able to mobilise resources and information, operating under norms of trust and reciprocity. Our main claim is that the connections we make online carry the strength to create meaningful social connections. These connections, in turn, can be the trigger for further political action.

Traditionally, this idea has been contested; there is a growing group of dystopian scholars that argue that information and communication technologies are damaging the way in which we connect and interact with others. In particular, we have seen the surge of theories about social isolation, lack of community sense, and individualism. Our work takes a more sceptical approach. We believe that technology is not deterministic, and that social behaviour is a deeper trend that does not completely depend on the way in which we choose to connect with others.

Our work focuses on three different cases. We use the typology devised by Bennett and Sergeberg of what they call “connective action”: organisationally-enabled (Chilean 2013 presidential election), organisationally-brokered (UK Enough Food for Everyone ‘IF’ campaign against global hunger), and crowd-enabled (Occupy movement in the US).

We rely on Twitter data from the three cases. For the Occupy case, we have access to the Occupy Research project and their datasets. For the IF campaign, we have collected data during the main periods of the campaign. In the case of the Chilean election, we focused on the period of official campaigning, i.e. one month before the election.

Measuring social capital is always a difficult task, mainly for the different conceptual distinctions that the literature makes. In our case, we borrowed the concepts from Ronald Burt and focused on two structural elements of social capital: closure and brokerage. Closure refers to how tightly connected the members of small groups or cliques are. This relates to the notion of bridging social capital or intragroup ties. Higher levels of closure are usually related to the formation of trust among the members of the group. That is, the more you know someone and the closer you get with them, the more likely are you to trust that person.

Another important determinant of closure is homophily: people are usually attracted to interact with people who share their same interests and values. This also relates with what Putnam calls the “dark side” of social capital, the creation of very tight groups that exclude non-members. Thus, connections across these highly tight groups are essential, and that is what brokerage does. Putnam calls this bridging social capital, and is related to different political outcomes, such as political stability, trust in institutions, and social inclusion.

We calculate the network metrics for closure (clustering coefficient) and brokerage (network constraint) for each network per week. We observe the evolution of these trends over time, and analyse their relationship. In a healthy social environment, closure and brokerage should not work against the other, and that is exactly what we observe in the online cases.

The next step was comparing these networks with different theoretical models that are aimed to explain social networks. We use the Erdos-Renyi algorithm to simulate random networks as a baseline test. Then we expand the analysis by simulating networks using the Barabasi-Albert, and the Watts-Strogatz algorithms, as shown in Figure 1.

Figure 1 – Network constraints across campaigns

Sajuria Fig 1

The most striking result concerns the Occupy case. According to Bennett and Sergerberg, technology can replace the role of organisations in allowing collective action to take place. Given the low transaction costs, enforcement against free riding was less relevant and, then, people could create similar forms of collective action without the need for organisations. However, our results from the Occupy case paint a different picture.

Given that Occupy is a case without the presence of formal organisations, we expected to observe this new form of social structures reflected in the networks. However, what we observe is a network with very closed small groups, but few connections among them. Conversely, in the cases where organisations do play a significant role (the IF campaign and the Chilean election), we observe higher and significant levels of bridging social capital. Figure 1 shows the levels of network constraint for each network over time. This is the metric developed by Burt, and measures how constrained a member of the network is in connecting with others. Put simply, the lower the constraint of a person, the higher the ability they have to broker social resources across the network.

In Figure 1, the observed values are compared to the theoretical simulations. In the case of the IF campaign and the Chilean election, the observed values present significantly higher levels of brokerage than the random networks (Erdos-Renyi), while the Occupy case shows lower levels of brokerage in comparison to any of the simulations. This is evidence that, at the structural level, crowd-enabled connections are not able to fulfill the same role as formal organisations.

Our research shows a new picture, perhaps more balanced, of the role of new technologies in social life. We do not believe that social media is damaging social capital, but we argue that, at least from an structural point of view, behaviour transcends the distinction between online and offline platforms.

The main limitation of our research is evident: we are not observing the content of communications across Twitter, but only their structure. This might lead to some measurement error in the way in which we approach the questions of social capital, but we also believe that the structural signatures of social capital are consequential to its content features.

This article is based on the paper ‘Tweeting Alone? An Analysis of Bridging and Bonding Social Capital in Online Networks’ in American Politics Research, co-authored with Jennifer vanHeerde-Hudson, David Hudson, Niheer Dasandi and Yannis Theocharis.

One should always play fairly when one has the winning cards: electoral viability and Twitter sentiment

With Jorge Fábrega, we we are currently working in a really interesting project to understand how public opinion surveys can be related to social media discussions. In a nutshell, we are interested in comparing public opinion data to what we can observe from Twitter. As a first step, we have prepared a draft chapter for an edited volume on digital methods for the social sciences, currently under review. The paper is still in a very early draft version, but I just wanted to share some of the early results.

The argument goes as follows. There is a great deal of research trying to use social media data (mainly Twitter) to forecast elections, and some research on how to compare Twitter to candidate’s support on opinion polls. In the case of the former, most attempts have failed, while on the latter,  Nick Beauchamp has managed to produce some interesting results. Our approach is slightly different: instead of looking at candidate support, we focus on support for certain policies. And instead of trying to predict support, we take one step back and think on other ways to relate both elements.

What came to our minds as a useful starting approach was to look at the sentiment on Twitter and how it relates to candidate support. We had two initial hypotheses:

  1. Supporters of candidates with higher electoral viability tend to tweet with a more positive tone overall.
  2. There is a correspondence between support for a policy and the tone used to tweet.

We used Chile as our case, and had two data sources: The CEP survey from October 2013, and Twitter data we collected previous to Chile’s presidential election last year.

Chile’s last election was quite competitive in terms of the number of candidates, but not in the vote share. President Bachelet had to go to a run-off election (something expectable with 9 candidates) against Evelyn Matthei, but she managed to get 62% of the votes on that round. A record share in the country’s recent history. In terms of electoral viability, 78% of the respondents of the CEP survey (2 months before the election) believed that Bachelet would win the presidency, with only 5% of people thinking that Matthei could get the job.

With that in mind, we processed our Twitter data to estimate two things: the political position of Twitter users in Chile, and the tone in which they tweet about 5 controversial topics:

  1. the possibility of constitutional reform through an assembly;
  2. a change in the current electoral system;
  3. the approval of an equal marriage law;
  4. abortion; and
  5. the ownership of the copper mines (they used to belong to the State).

To estimate the political position, we used retweet networks. In simple words, we setup an initial group of Twitter users with known preference for a candidates, and then performed some social network analysis of those who retweeted them. From there, we could move on to assign a probability for each user to support one of the candidates. We focused only on the four main candidates (Bachelet, Matthei, Enríquez-Ominami and Parisi), since they were the ones that generated more attention during the campaign.

To estimate the tone, we translated the lexicon from Wilson et al. and crafted our own version of the sentiment R package (used for sentiment analysis, of course). Each tweet was deconstructed into single words, which were then compared to the lexicon. Each word in the lexicon had assigned a polarity (positive or negative), and whenever there was a match between the words from the tweets and the lexicon, we assigned that polarity to the word. Then, the package uses the voter algorithm to determine that, given the number of matches, that tweet was itself positive or negative.

We first calculated the effect of candidate support on support for the policies mentioned above. our results (shown in the figure below) show that most respondents who supported one of the four candidates, in comparison to those who supported another or none, also support the policies. This was statistically significant for all the cases with a * in the figure.



In the case of Twitter, since our sampling was not probabilistic, we were more concerned about the direction of the relationship rather than the significance. Our dependent variable was the likelihood of a tweet having a positive tone. The results can be seen in the figure, and the most surprising feature is the decrease in probability for a positive tone when talking about equal marriage.

As we expected, supporters of Bachelet had a higher tendency to tweet with a positive tone, overall. Also, whenever there is a statistically significant relationship between supporting Bachelet and supporting a policy (e.g. electoral reform, abortion or copper mines ownership) that had a correlate with an increase in the probability of a positive tone on Twitter.

However, our second hypothesis suffered from a different fate. As you can observe, there is some decoupling between the support for a policy and the likelihood of a positive tone. Let’s look at the case of supporters of Enríquez-Ominami. aAlthough they seem keen to support all of the reforms, the tone they use when they tweet is not positive in most cases. We see a similar behaviour in some of the supporters for other candidates as well

We are still in the stage of making sense of these results, and thinking about possible expansions of this research. In the meantime, this is a nice starting point, and provides some insightful information on how to use Twitter data, not to replace, but to complement classical public opinion research.

Twitter oblivious to Farage’s media mauling as EU polls open

** UPDATE: Extended methods section at the bottom of the post
This article was originally published on The Conversation. Read the original article.

By Orlanda Ward, University College London and Javier Sajuria, University College London

Nigel Farage, leader of the UK Independence Party, appears to have stolen the show in the run up to the European elections. But while he has been pilloried in the papers, discussion about him on Twitter appears to have been somewhat more favourable.

Since campaigns for the European elections have been largely fronted by party leaders, we’ve investigated the level of mainstream media coverage given to David Cameron, Nick Clegg, Ed Miliband and Nigel Farage over the past six days. We’ve also looked at how much discussion has been going on about the leaders on Twitter over the same period.

We looked at all geo-tagged UK tweets and all national tabloid and broadsheet newspaper coverage of the Conservative, Lib Dem, Labour and UKIP leaders over the period. We analysed the amount of coverage and discussion each party leader received online and offline each day, and the proportion for each that was positive, negative or neutral.

Not a fan of the coalition. 

While there has been plenty of mainstream newspaper coverage of major party leaders in the run-up to EU elections, particularly focusing on Farage, this has not been reflected in online discussions. For a start, political discussion only featured in about 2% of the almost 3m tweets we monitored.

Overall, both online and offline, Cameron and Farage have been the most prominent, trailed by Miliband and Clegg on both platforms.

But Farage in particular has been the subject of very different coverage in the online world and the more traditional press. While the UKIP leader’s media coverage spiked immediately following his now infamous LBC interview, his mentions on Twitter suggest that the online reaction was more of a slow burn, though the tone of the discussion did become slightly more negative. Perhaps the LBC episode failed to inflame passions online becuase it simply seemed to confirm what existing views of Farage – both for and against.

Party leader mentions in newspapers and on Twitter 

What’s more, the intensely negative coverage of Farage in the mainstream media has not been replicated online. If you only read the papers, you’d find that 31% of the comments made about Farage were negative, while between 20-21% of those made about Miliband, Clegg and Cameron could be classed as such.

But the proportion of tweets mentioning Farage that were negative was near identical to Miliband and Clegg at between 22% and 23%. Cameron got an easier ride with just 13% of tweets about him coding as negative.

Tone of mentions of Nigel Farage 

No less than four simultaneous campaigns have been bubbling away in the UK as we carried out this analysis. While the European and local elections have fired the starting gun for the general election, the Scottish independence debate is also in full swing, not to mention the addition of a possible EU referendum.

This has meant that discussion and coverage this week has been fragmented. Miliband has spent the week laying out his policy for the general election amid claims that he’s been missing in action when it comes to European campaigning.

Cameron of course spent two days on a pro-union visit to Scotland, and much of rest of his exposure concentrated on the Chilcot Inquiry and coalition tensions. Clegg, meanwhile, has gained attention for all the wrong reasons: drunken cactus shame; losing his rag with Michael Gove and Andrew Marr; Commons whispers of a Lib Dem deposition and some polls suggesting the party may well fall behind the Greens in Europe.

In contrast, although reporting on Farage was dominated by the fallout from his LBC interview and questions about whether he is or isn’t a racist, it did stay focused on the strength of his party’s electoral prospects and his stance on immigration. What else is there to talk about?

Farage has become the focal point not just for the media, but for the major party leaders this week – that is, when they weren’t focusing on other elections. This short campaign has seen little coherent debate between parties and while their antics of course top the printed press agenda, our data suggests that they are not engaging debate among the wider public.

It also suggests that media lambasting of Farage doesn’t look set to change voters’ minds – at least not those on Twitter.

Methods note

As a response of some of the questions asked about the methods we used to collect and analyse the data, we offer here some basic explanation.

For the Twitter data, we used the streamR package to collect the data from the Twitter streaming API. We focused only on geotagged tweets in the UK, which accounted for an average of 500,000 tweets a day. Some research shows that the streaming API allows us to get most (if not all) of the geotagged tweets in a given period, but we also n=know that no more than 2% of the tweets contain geographical metadata. This is obviously a bias, and we try to be as explicit as possible about the limitations of our data and methods.

The newspaper data was obtained using Nexis and getting all articles that contained the name of one of the UK party leaders. As explained above, the number of mentions for the leader of the Green Party were too low (or nonexistent) and we had to remove her from our sample. After we obtained the articles, we trimmed the sentences where the party leader name was mentioned, and that was our unit of analysis.

In terms of analysis, we made some changes to the methods used by Pablo Barberá on his workshop at SMaPP NYU. Most of the R functions created for this purpose were compiled in a (very beta) R package called euElection. This package allowed us to obtain a very rough estimate of the tone of the tweets and the newspapers sentences, which is what we used in the article. In simple terms, each word from our units of analysis was compared to a vocabulary of positive and negative words (the “lexicon). All other words were considered as neutral. Then, we obtain the proportion of tweets/sentences that has a majority of positive, negative or neutral tone. We only selected tweets and sentences that mentioned, at least, the name of the party leaders.

We are happy to receive any feedback or comments you may have on this and other methods issues.

The Conversation

APSA 2013 Twitter report


Following last year’s humble attempt to provide some insight from the twitter conversations around #APSA2012 (specially considering the last minute cancellation of the conference) – and given that other duties restricted me from attending APSA this year 🙁 – I will be collecting and displaying some data from this year’s conversations. There will be more updates throughout the conference. If you want to follow the chronological reports, you need to start from bottom to top.

Short methods note: Edges are created by mentions, replies or re-tweets. Nodes are coloured according to the components, and their size is scaled according to eigenvector centrality. Isolates (ie. people not talking to anyone but using the hashtag #APSA2013) are not included.


1. DATA: Someone asked me for the data I used to produce this post, and I strongly believe in the importance or replication. Here it is a list of all the tweet IDs I used. Sorry, but that’s the only way I can share it without violating Twitter’s TOS –> DATA

2. I plotted all the geotagged tweets against the map of Chicago. This gives a better sense of where the tweets where concentrated around the city.


UPDATE 10 (AND FINAL): A few comments before I introduce the data. This exercise had two purposes. First, I wanted to freshen up my skills on Twitter data collection and analysis. After spending part of the summer learning a lot on Python, R and SNA (mainly thanks to the International Summer School 2013 “Social Network Analysis: Internet Research”), I decided that an extension of last year’s analysis on the APSA tweets would be a good opportunity. In total honesty, I hope you enjoyed it too. Second, my research agenda uses extensively this type of social media data to draw inferences about political behaviour. Although this particular exercise was extremely self-centred, since I’m focusing on the interactions in a Political Science conference, it provides some insight on what social media data can tell us, and how can we use it to make sense of bigger issues. That’s why I decided to write this other post on Obama’s speech this week, to show some “real life” examples. Also, I realise that I’m not new on this field, and that there are amazing people working on these issues for a long time (most of them with much more sophisticated analyses than mine). I believe in building community, so I tried to attribute their work where appropriate and link to their own websites and Twitter accounts. I extremely recommend you to follow them and their research. Finally, this post will eventually become a paper-like longer post, with more descriptive data and some interesting questions to test. I can’t promise when, but it will come.

Ok, now let’s go to the data analysis. Joshua Tucker (NYU) tweeted today his excitement for being in the “top ten vertices” list from a Twitter SNA made by Marc Smith, using NodeXL. I’ve used NodeXL in the past (and I believe is an amazing off-the-shelf tool for Windows user), but its reliance on the Search API made me realise that I could get better results by downloading the data via the streaming API for the full duration of the conference. It requires more time and resources, but the results are much more informative. Then, I decided to create my own top ten, but using eigenvector centrality instead of betweenness centrality (as in the NodeXL list). The reason is simple: the former relies on the relative importance of the connections of a node. That is, if the people I interact with are more “important” (or central) in the network, I become more important too. Betweenness centrality, on the other hand, focuses on who are the bridges across different nodes, who is more able to connect the rest. Although that is usually an important question in network analysis (actually, I co-authored a paper with Jorge Fábrega where we use it extensively), in substantive terms eigenvector centrality seems more appropriate for the type of network we have here. With that info in mind, here are the winners:

Table: Top 10 accounts according to their Eigenvector Centrality.

1 @APSAtweets @andrew_chadwick @dandrezner @APSAtweets @APSAtweets
2 @texasinafrica @davekarpf @25lettori @dandrezner @dandrezner
3 @abuaardvark @rasmus_kleis @rasmus_kleis @mqsawyer @andrew_chadwick
4 @APSAMeetings @kreissdaniel @andrew_chadwick @LarrySabato @ezraklein
5 @dfreelon @OUPAcademic @APSAtweets @TerriGivens @texasinafrica
6 @CambridgeJnls @insidehighered @mikejjensen @insidehighered @APSAMeetings
7 @andrew_chadwick @FUNGLODE @ezraklein @j_a_tucker @abuaardvark
8 @ProfCaraJones @Worse_Reviewer @StephanieCarvin @monkeycageblog @davekarpf
9 @dandrezner @j_a_tucker @TerriGivens @washingtonpost @raulpacheco
10 @zizip @abuaardvark @raulpacheco @APSAMeetings @ProfCaraJones

In terms of volume, day 4 was the smallest one. With only 114 nodes and 141 edges, the conversations were less frequent. A possible explanation is that most of the delegates had already gone by then, and only those who had panels on that day were staying around the conference venues. The clusters are a bit more institutional, with high prominence form APSA’s official accounts, along with some blogs and websites (such as @monkeycageblog and @insidehighered). A new addition is Larry Sabato, from U. Virginia



The cumulative network does not show many differences from yesterday. This is not surprising, because most of the activity took place before, and most of the communications were between people who already tweeted each other before. The new interactions might have added some weight to the already existing edges, but not much more. In any case, here is the final network of the APSA 2013 Annual Meeting:

APSA 2013 final graph - 868 nodes and 1794 edges.
APSA 2013 final graph – 868 nodes and 1794 edges.

UPDATE 9: Day 3 was clearly quieter than the precious two. A bit of it might be the classical effect of people leaving after they present, or simply wandering around Chicago. It might also be that the panels are becoming increasingly more interesting, and people prefer to pay attention to the presentations instead of tweeting ;). In any case with all the fuss around President Obama’s speech on Syria (Hint: I recently published a quick report on that), I was expecting that IR crowd attending the conference would be very active. Well, just by simple observation of their accounts, they were, but did not necessarily use the #apsa2013 hashtag to express their views. That said, @dandrezner and @ezraklein are some of the “stars” of today’s network, with a high level of eigenvector centrality. The Political Communications cluster remains active with @andrew_chadwick, @rasmus_kleiss, and @25lettori leading the way (clearly a clique around Royal Holloway’s New Political Communication Unit).

Day r of #APSA2013. 253 nodes and 325 edges.
Day 3 of #APSA2013. 253 nodes and 325 edges.

Moving on to the cumulative graph, the network is not becoming much bigger (832 nodes in total). This reflects the lower number of conversations from day 3, but also that some ties are already established and some people keep talking to each other. The APSA team is doing really well in driving the conversation, with @APSAtweets and @APSAmeetings as really central nodes in the network. As expected, those who were central yesterday, remain so today, so no news on that regard. All in all, the network seems to be coming to a point of “convergence” or “stability”, with conversations taking place among the same members and with no significant cliques outside the big group. The question of inter-field dialogue remains open, as some relevant nodes in the network belong to different components (such as @ezraklein in comparison with the rest of the bigger component).

Cumulative network at day3. 832 nodes and 1667 edges
Cumulative network at day3. 832 nodes and 1667 edges


(QUICK) UPDATE 8: Using Pablo Barberá’s StreamR package (along with ggplot2), I mapped the tweets that had location data in them (only 19 out of 1321). Not surprisingly, most of them are highly concentrated in Chicago, but a couple appear to be somewhere else in the US. This goes towards question whether people not attending the conference are getting any benefit by tweeting about it. There were no geolocated tweets outside the US, in case you were wondering.

Geolocated tweets in red.
Geolocated tweets in red.

UPDATE 7: This is the final summary of day 2. The next 2 days I aim to produce just one daily report, so you’ll have to bear with me. Again, I present two graphs. The first one is the full network for all the days of the conference (including pre-conference events). The second one contains all tweets captured at day 2 until 7.30pm.

The cumulative network shows again a big component in pink, but the network is becoming much more diverse than in previous iterations. More clusters appear, while others that were disconnected (such as the one lead by @funglode) are now connected to the bigger network. The usual suspects remain as key actors in the network, and depending on the volume of tweets over the weekend, they will probably remain in that position. Some well-known IR scholars do not belong to the bigger component, which is an interesting phenomenon. If we look at @ezraklein or @SlaughterAM, they are connected to the big network, but form clusters around them (perhaps the cross-field conversations are not as clear as I thought) The Political Communications group is highly active, especially @andrew_chadwick@zizip and @davekarpf (who also shared a widely tweeted panel today, which might also account for their relevance in the network).

An important notice is that this exercise is, in some way, a performative process. While I publish these networks, some people become aware of their own position and the people they interact with. That is always something to take into consideration when doing the analysis, which brings some epistemological discussions to the table (this is like Schrödinger’s cat reporting on its own experiment).

Cumulative network at August 30, 7.30pm. 704 nodes and 1384 edges
Cumulative network at August 30, 7.30pm. 704 nodes and 1384 edges
Network for day 2 (30 August). 375 nodes and 635 edges.
Network for day 2 (30 August). 375 nodes and 635 edges.


UPDATE 6: This time I’m bringing two graphs. The first one corresponds to the cumulative network. That is, the Twitter conversations from the pre-conference events until the last update. The second graph corresponds only to the conversations taking place during day 2 until 1pm CT. As you can notice, there are similarities among the networks, such as the existence of a big component in the middle (the cumulative network uses strongly connected components to colour the nodes). However, the central actors vary a bit. There are some accounts that remain relevant and central to the network, such as @apsatweets@dandrezner@texasinafrica@raulpacheco@ezraklein and @j_a_tucker. However, we can observe some new actors coming into the scene, such as @heathbrown and the institutional account for @insidehighered. Also, there is an interesting cluster formed by @funglode and @anniavaldez, formed mainly by Spanish-speaking users.

The field boundaries seem more diffused now, which brings questions about whether conferences actually create the opportunity for cross-field dialogue. There are several panels trying to analyse the overall role of Political Science, and how can we communicate better with our audiences. Maybe that is driving a lot of the conversations. That’s an interesting hypothesis to test. Another interesting fact is that some central nodes are people who are not attending APSA this year (such as myself :)). This also brings a question about who benefits from the conference, and if it is necessary to attend to obtain some basic returns from it. Obviously, we need to get data from other sources outside Twitter to find that out. In the meantime, this has become more than a simple exercise of mapping APSA.

Cumulative network from the pre-conference events until August 30 at 1pm. 603 nodes and 1113 edges.
Cumulative network from the pre-conference events until August 30 at 1pm. 603 nodes and 1113 edges.
Network for day 2 (August 30) at 1pm. 223 nodes and 323 edges.
Network for day 2 (August 30) at 1pm. 223 nodes and 323 edges.

UPDATE 5: This graph is a lot bigger than the previous one, as it brings together the data form the pre-conference events plus the day 1 (August, 29). Thanks again to @jorgefabrega for the help using the Search API to retrieve that data (I know, the search API might not be the best option to get an accurate picture, but it’s the only one I had available. If you want a thorough discussion of the representativeness of the different Twitter APIs – mainly the Streaming API – I would definitely encourage you to look at Mostatter et al. 2013)

Back to business. I made some small changes to the visualisation this time. I used strongly connected components instead of weakly connected components. First, it made more sense since the network is directed. Second, with the weakly connected component we got a big group in the middle where almost everyone was connected, which is not true. Also, one of my goals is to analyse the networks and try to make a comparison by sections/fields affiliation (if anyone is interested in helping with that, please let me know in the comments section!). This time we have 479 nodes and 823 edges.

I’m currently collecting data from today’s sessions, and will provide a daily graph and an accumulated one. Let’s see how that works. As usual, feedback is more than welcome.


UPDATE 4: Last graph of the day (it’s pretty late here in London). This corresponds to an accumulated network of the entire first day of #APSA2013 until 7pm, Chicago time. Now the network is much bigger than the previous one (it seems that conversations take some time to build up) with 327 nodes and 489 edges. The clusters we saw in the previous graph are much more diffused now. We can observe a big central component (in green) that connects most members of the network. However, it is possible to observe some patterns in the conversation that can be attributed to different fields or the type of Twitter accounts (oddly enough, publishers’ accounts tend to mention and re-tweet each other).

Tomorrow morning I will aim to produce a larger accumulated network with info from the pre-conference events (thanks to Jorge Fábrega for his help on getting that data). Also, I aim to produce the accumulated version and a daily one. Let’s see if we can get something from these dynamic networks. I hope you are enjoying the conference, stay tuned!

Full network of day 1 of APSA (August 29)

UPDATE 3: At 3pm Chicago time, things got much more complex and ‘networked’ (pun intended). At this point there are 167 nodes (ie. Twitter accounts) and 261 edges (defined by mentions, replies or re-tweets. We can observe a big cluster in the middle (in dark orange) where the APSA official account (@APSAtweets), alongside some well recognised political science/tweeters, such as @dandrezner@raulpacheco, and the recently acquired by the WaPo, @monkeycageblog. Another recognisable cluster (in pink) is the one formed by the Political Communications scholars, such as @zizip@andrew_chadwick@davekarpf, and @abuaardvark)

Network at 29 August, 3pm CT

UPDATE 2: This is the network at 12pm. As you can see, the groups are getting bigger and tighter as the conference evolves.

Network at 29 August, 12pm CT

UPDATE: At 9am in Chicago, this is how the network looks like.

Network at 29 August, 9am CT

Note: Thanks to Alex Hanna for his small – yet crucial – advice on how to build the networks.

Quick report: Obama’s speech on Syria

President Obama made a speech today explaining the US position on attacking Syria (more details here, here, and here). Luckily, I was collecting data on the APSA 2013 conference, so I managed to run a small script and collect some tweets during the speech. It’s a bit early to get a good idea of the tone and the substantive info we can get from there, but for now, let me show you how the tweets are geographically located. Out of ~5000 tweets I managed to collect, only 1% of them had location coordinates, which is pretty much the usual rate. I plotted all of them against a world map, and here is the result. tweets_obama_syria   Now, if we perform some basic network analysis using the data, most of the nodes with bigger centrality were news outlet and the official account of Barack Obama (@BarackObama). Most of the edges correspond to people re-tweeting mainstream media accounts, while others were simply making their own comments. The network shows how all these people interacted during the first 8 minutes of the speech.

Network of tweets mentioning 'Obama' or 'Syria'. Ties represent mentions, replies or RTs, colours correspond to weakly connected components, and the size of the nodes reflects the eigenvector centrality score of each account.
Network of tweets mentioning ‘Obama’ or ‘Syria’. Ties represent mentions, replies or RTs, colours correspond to weakly connected components, and the size of the nodes reflects the eigenvector centrality score of each account.

Finally, I perform some (really) basic sentiment analysis on the tweets of the first 8 minutes. The method was designed by Alex Hanna, from U. Wisconsin – Madison, and I used the list of words developed by Neal Caren, from UNC – Chapell HIll. This also means that words in other languages than English were not coded. The scores are calculated by minute, and they all stay very close zero. However, the sentiment was more negative at the beginning of the speech and ended up being positive. Uncertainty measures are not provided by this (very brute) way of calculating the sentiment, so it’s not possible to know if they are significantly different than zero (this was a boring caveat, but an important one). sentiment   This is all for now. As you can see, there is nothing about IR or geopolitics in this post. Is mainly a way to show how Twitter data can give us a fast (and sometimes overwhelming) way to analyse current events.