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Twitter Conversations on UK E-Petitions
- 1. Prof. Cristina Leston Bandeira, School of Politics and International Studies Dr. Viktoria Spaiser, School of Politics and International Studies With Molly Asher, Data Science Intern, Leeds Institute for Data Analytics What do Twitter conversations tell us about petitioning?
- 2. • 2015 • 10,000 signatures (government response), 100,000 signatures (considered for a debate) • Wide ranging public engagement initiatives – including Twitter • Aims: o open up, o give a direct channel people–parliament, o enhance relationship Background
- 3. Key question: Do petitions enhance engagement with Parliament? • New system’s processes: clear and transparent. • But are they perceived as worthwhile by the public? • Debate: the highlight of petitioning. How is it perceived by the interested public? • Petitions debated March-Nov 2016 • Ban grouse shooting petition • Comparison of reactions to debate and oral evidence session • #GrouseShooting Background
- 4. 1. What can Twitter conversations tell us about the extent to which individuals engage in the petition process beyond the signing stage? 2. How are individuals engaging through Twitter with e-petitions and to what extent do their discussions inform us about their reactions to the e-petition process? 3. Who gets involved in conversations about e-petitions and how do they interact? • Collection of all Twitter data tagged with a specific hashtag (defined by Petition Committee) • Twitter Streaming API Twitter Data
- 5. • Data processed, analysed and visualised with Python, R and Gephi • Extracting key information, e.g. how mange Twitter users participated in a petition debate on Twitter • Topic identification: N-gram based semantic network • Social Network Analysis based on retweet patterns • Automatic Sentiment analysis with AFINN-111 online lexicon • Twitter User Classification with Naïve Bayes Classifier • Quadratic Assignment Procedure (QAP) analysis to test homophily Data Analysis nodes = words/Twitter users weighted edges = frequency of co-occurrence of two words/ retweet frequency + community detection algorithm (Lovian method)
- 6. What can Twitter conversations tell us about the extent to which individuals engage in the petition process beyond the signing stage? Title of issue Hashtag Number of signatures Number of tweets Number of users involved Invoke Article 50 of The Lisbon Treaty immediately. #exitingtheeudebate 127,111 32 23 Debate in the House the Local Government Pension Scheme Investment Regulations #lgps 105,772 25 23 Ban driven grouse shooting #grouseshooting 123,077 7,364 2,704 Urge the South Korean Government to end the brutal dog meat trade #DogMeatTrade 102,131 2,997 1,113 Stop retrospective changes to the student loans agreement #StudentLoanDebate 133,969 86 81 Include expressive arts subjects in the Ebacc #EbaccDebate 102,499 3,283 1,451 Stop spending a fixed 0.7 per cent slice of our national wealth on Foreign Aid #UKAidDebate 235,979 7,474 3,092 Stop Cameron spending British taxpayer's money on Pro-EU Referendum leaflets #EUReferendumLeafl et 221,866 48 41 Give the Meningitis B vaccine to ALL children, not just newborn babies. #menb 823,348 141 87 Keep the NHS Bursary #NHSBursary 162,568 447 292 The DDRB's proposals to change Junior Doctor's contracts CANNOT go ahead. #JuniorDoctors 110,065 224 176 Make an allowance for up to 2 weeks term time leave from school for holiday. #termtimeholiday 127,199 4 4 Fund more research into brain tumours, the biggest cancer killer of under-40s (April) #braintumourresearc 120,129 630 282 EU Referendum Rules triggering a 2nd EU Referendum #EURefDebate 4,149,757 6 4 Make it illegal for a company to require women to wear high heels at work #heelsatwork 152,420 272 75 Restrict the use of fireworks to reduce stress and fear in animals and pets #FireworkDebate 104,038 92 50 Results
- 7. How are individuals engaging through Twitter with e-petitions and to what extent do their discussions inform us about their reactions to the e-petition process? • RT @Third_Position: Anecdotal gibberish. #GrouseShooting has been around for centuries & hunting is ancient. They help conserve these animals • RT @WildlifePhelps: "We are losing a species and this is a crisis" Finally an MP states the real reason for the petition! Excellent. #grousesooting • Red_eyed_video "If that was a debate it's no surprise the country's in a mess“ #grouseshooting Results
- 8. How are individuals engaging through Twitter with e-petitions and to what extent do their discussions inform us about their reactions to the e-petition process? Topic cluster: Positives of hunting • Masculinity • Primal • Father • Son • Bond • Thirdposition • Shot • Food Results
- 9. How are individuals engaging through Twitter with e-petitions and to what extent do their discussions inform us about their reactions to the e-petition process? Topic cluster: Consequences of hunting • Heather • Flooding • Henharrier • Ecology • Calderdale • Environment • Review • Study Results
- 10. How are individuals engaging through Twitter with e-petitions and to what extent do their discussions inform us about their reactions to the e-petition process? Topic cluster: Criticism of Debate • Frustrating • Polarised • Bias • Praising • Favouring • Stevedouble
- 11. Oral Evidence session • Allows petition creator and others to present evidence • Difference in reaction to between the forms of parliamentary sessions How are individuals engaging through Twitter with e-petitions and to what extent do their discussions inform us about their reactions to the e-petition process? Results
- 12. How are individuals engaging through Twitter with e-petitions and to what extent do their discussions inform us about their reactions to the e-petition process? Results
- 13. How are individuals engaging through Twitter with e-petitions and to what extent do their discussions inform us about their reactions to the e-petition process? Results
- 14. Who gets involved in conversations about e-petitions and how do they interact? • The size of the nodes show their centrality • Most influential users are already politicians/campaigners • Nodes of same colour interact with each other more than with others of different colours Results Debate Oral Evidence Session
- 15. Who gets involved in conversations about e-petitions and how do they interact? Naïve Bayes Classifier results + Quadratic Assignment Procedure (QAP) analysis suggests significant homophily in the social network retweeting those you already agree with Polarisation and closed communities Results
- 16. • Conversations on twitter can help us to understand more about the e-petitioning system: 1. Often people sign a petition and then forget about it. The number of people who engage on Twitter varies massively. Protest vs Substantive petitions 2. People are using Twitter as a means of publicising debates and pressuring their MPs to attend, but also as a means of expressing views on parliamentary process. Difference in reactions between debate and oral evidence suggest reflection on nature of debates. 3. Twitter conversations show homophily and polarisation: online discussions reinforcing people’s views rather than contributing to wider consideration of points of view. Though, less so when @HoCPetitions live tweets. Suggests it can play useful role to consider range of arguments on petition. Preliminary conclusions
- 18. What can Twitter conversations tell us about the extent to which individuals engage in the petition process beyond the signing stage? • E-petition just the first stage • Is signing a petition too easy? • Is it ‘slacktivism’? • Twitter is one way to maintain engagement, but how many people use it…..?
- 20. How are individuals engaging through Twitter with e-petitions and to what extent do their discussions inform us about their reactions to the e-petition process? Word 1 Word 2 Weight debate grouseshooting 1931 ban grouseshooting 853 grouse grouseshooting 739 driven grouseshooting 722 grouseshooting mps 567 ban debate 487 debate mps 483 grouse shooting 475 grouseshooting moors 403 ban driven 378 debate grouseshooting “You may know a word by the company it keeps” – John Firth
- 21. How are individuals engaging through Twitter with e-petitions and to what extent do their discussions inform us about their reactions to the e-petition process? Ignoring of Lucas intervention • Depressingly • Peat • Dismissed • Importance • Highlight • Climatechange • Uplands • CarolineLucas
- 22. How are individuals engaging through Twitter with e-petitions and to what extent do their discussions inform us about their reactions to the e-petition process? Tweets: Debate • Chrisgpackham • natalieben • leagueacs • markavery • campaigners • Directed • Attack • Vitriol
- 23. How are individuals engaging through Twitter with e-petitions and to what extent do their discussions inform us about their reactions to the e-petition process? word n Sentiment score Overall sentiment Sentiment Contribution admit 67 -1 negative -67 appalled 94 -2 negative -188 attack 91 -1 negative -91 ban 1,357 -2 negative -2714 benefit 47 2 postive 94 bias 366 -1 negative -366 care 72 2 positive 144 crap 24 -3 negative -72 cruel 79 -3 negative -237 dead 64 -3 negative -192 death 137 -2 negative -274 depressed 113 -2 negative -22
- 24. Who gets involved in conversations about e-petitions and how do they interact? User 1 User 2 Weight 0GreenCat Greens4Animals 28 saysPRnow Greens4Animals 27 rdepledge Gameandwildlife 26 Greens4Animals Greens4Animals 21 Londononlytime Third_Position 21 kevinjholmes Gameandwildlife 20 kevinjholmes ShootingTimes 20 pip_tp ShootingTimes 10 BASCsimon Gameandwildlife 9 countrygent9 Gameandwildlife 9 0GreenCat Greens4Animals
- 25. Who gets involved in conversations about e-petitions and how do they interact? Pro Anti Pro Anti ProAnti Anti Anti
Notes de l'éditeur
- It’s been possible to petition the government in this way since 2006 when this facility was created for the first time on the No.10 Downing Street Website. Since then the e-petition system has gone through several re-incarnations, and in July 2015 the system as we know it was launched. The way it works is that if a petition gets over 10,000 signatures then it will receive a government response, and if its over 100,000 then it will be considered for a debate in parliament. As part of the new system there is now a Petitions Committee who oversee the process and they are also trialling a number of other ways of considering the content of the petition, such as hosting oral evidence sessions, digital debates or gathering evidence on online forums. And of particular importance to what I’m talking about today, if a petition goes to debate then they set up and publicise a hashtag with the hope that members of the public will use it to discuss what’s going on in parliament. The aim of all of this is to open up a means of two way dialogue and to provide a route through which any member of public feels they can connect with parliament.
- It’s been possible to petition the government in this way since 2006 when this facility was created for the first time on the No.10 Downing Street Website. Since then the e-petition system has gone through several re-incarnations, and in July 2015 the system as we know it was launched. The way it works is that if a petition gets over 10,000 signatures then it will receive a government response, and if its over 100,000 then it will be considered for a debate in parliament. As part of the new system there is now a Petitions Committee who oversee the process and they are also trialling a number of other ways of considering the content of the petition, such as hosting oral evidence sessions, digital debates or gathering evidence on online forums. And of particular importance to what I’m talking about today, if a petition goes to debate then they set up and publicise a hashtag with the hope that members of the public will use it to discuss what’s going on in parliament. The aim of all of this is to open up a means of two way dialogue and to provide a route through which any member of public feels they can connect with parliament.
- The hashtag serves to coordinate all debates on a Petition debate in the Parliament With this data we hope to answer the following questions: However, because e-petitions exist online there is automatically data generated about interactions people have with them and this presents an opportunity to analyse the way people perceive them in a different way. So as I mentioned earlier if a petition is chosen for debate then the parliamentary sessions are live-streamed and the petitions committee set up a common hashtag for the debate through which discussions can be had on the issue on twitter. The twitter streaming API can be used to collect all tweets made to this common hashtag, allowing us to listen into this twitter conversation. Through analysis of this conversation and of those taking part, its hoped we can assess if e-petitions are achieving their aims and to do this we’ve defined three questions which we want consider. For the rest of the presentation I’m going to introduce each of these questions, the importance of it and what our analysis has discovered.
- Text as a network, with words being nodes and edges signify if words were collocated within a pre-defined distance window (best 5 words incl. the two respective words). Edges weighted, frequency of co-occurrence Community detection through Lovian method (maximing modularity): attempts to optimise the network modularity. Modularity measures the density of edges inside communities relative to the density of edges outside communities. In large networks this optimisation has to be achieved through heuristic algorithms, since it would be impossible to go through all possible combination iteratively. The Louvian method detects first small communities by optimising modularity locally, then these small communities are grouped and the modularity optimization is repeated. We found that this approach produced better topic detection results than the more common Latent Dirichlet Allocation topic modelling approaches. Force Atlas2 algorithm for layout AFINN-111 online lexicon containst sentiment words with pre-coded scores between -5 (very negative) and +5 (very positive), 0 is neutral We can calculate average sentiment of debate based on scores and frequency of certain negative or positive words
- One way an individual can maintain their engagement with the petition process is by taking part in the Twitter conversation at the time of the debate. The table here provides an overview for each of the petitions of how many signatures they attracted, how many tweets were made about them and how many users took part in the conversation. From this we can observe that the level of engagement once the petition goes to debate varies massively, with the highest number of tweets on a single debate 7,500 and the lowest, just 4. In particular, it is interesting to note that a petition on exiting the EU, which received over 4 million signatures, an order of magnitude higher than any other, had only 6 tweets written about it. This shows there is not a straight forward relationship between initial engagement and continued engagement. So what factors might result in continued engagement? Research into the factors that lead to the success of the petition in the signing stage have suggested that coverage of the petition in the media and the number of signatures a petition receives in its first day are key determinants of its success. This suggests there is a tendency to follow the crowd. Individuals may sign a petition receiving lots of support if they at least vaguely agree with its cause but this level of interest is probably not enough to convert into further action. However, those who sign a less well publicised tweet are more likely to have sought it out themselves, have genuine and long standing interest in the cause and be more willing to follow through on their signature.
- In this research we wanted to gain some understanding of this through looking at the kind of topics being covered in the twitter discussion. A quick glance at the tweets showed there was a range of feelings being expressed, and there were some worryingly negative sentiments being expressed. however, we have thousands of tweets on this topic and to read of all of them would take a long time, and so the aim was to find a process to infer the topics being discussed without having to manually read each tweet.
- The cluster in grey demonstrates that the opposition group, who support grouse shooting and oppose the ban were also present on twitter. The words found here reflect discussion of the idea that hunting is a primal activity allowing bonding between father and son, and a right that should be protected.
- Finally, the purple cluster makes up the only topic which is more factual, covering the environmental impacts of burning heather moorland, which is something that happens to allow grouse shooting to take place, and there is reference to flooding, ecology and the environment, and to Calderdale, an area with managed grouse moorland that was hit badly by flooding. It also mentions sources of factual evidence such as studies and reviews.
- So…by looking at this network we can pick out a number of topic clusters. I’m wasn’t sure how easily you’ll be able to see this, so I’ve drawn a ring around several of the clusters and picked out some of the key words from them. The first, highlighted here, relates to criticisms made of the way the debate was hosted, and words include “frustrating, polarised, bias, praising.” This reflects a feeling that the debate was biased and that the leader of the debate, Steve Double, was giving much more time to opposing the ban than to supporting it.
- I also constructed a network from the conversation on twitter about the Oral Evidence session. I won’t go through this in the same detail but it is possible to observe differences in the nature of the discussion. The conversation is more factual and less polarised, although members from both sides of the debate are still present. There is less critique of the parliamentary process, with some praise of the clarity of arguments presented by the side opposing the ban and some calls for more evidence to allow proper assessment of the case. The difference in the reaction on twitter to the two parliamentary sessions is interesting and may tell us something about the effectiveness of the e-petition process. For instance, the more negative reaction to the debate may be a result of the way the process is carried out. Whereas in the Oral Evidence Session the creator of the debate and other individuals are invited to give evidence, in the debate the issue is discussed solely by MPs in the same format as any other debate. The results, of this analysis at least, suggest that members of the public may feel that this process does not take sufficient account of the views of the public and the petitioner, and grants unfair weight and bias towards the kind of groups who usually hold political favour. Considering the aims of the e-petition system are to increase trust in parliament and to give voice to the concerns of the public, this would suggest that this is a flawed process.
- So, these are the plots I’ve made for the Twitter conversations, on the left is the debate and on the right is the oral evidence session. Examining these kind of backs up what we’ve already discussed, and that is that the content of the Twitter conversation was much more negative than that about the oral evidence session. The cross over between positive and negative words is higher up horizontally for the Twitter conversation, showing a larger variety of negative words being used, and the cross between positive and negative is also much further to the right, showing that the frequency and severity of the negative words was also much greater. Comparison of the types of words also finds those in the conversation on the debate to be more emotive ones, unacceptable, ridiculous, disappointed, disgusting, compared to more factual ones for the oral evidence session
- Looking at all four of the plots side by side shows that the relationship between the sentiment of the Twitter conversation and the sentiment of the transcript is reversed between the two sessions For the debate the Twitter conversation is much more negative than the transcript, Whereas for the oral evidence session the opposite is true, and the transcript is more negative than the Twitter. This all ties in with the idea that the debate was being criticised for allowing more time to the side of the argument which was supporting grouse shooting and supports our earlier concerns that the e-petition process is being perceived by some members of the public as biased and unfair.
- From this we can construct a network, which was also done in the same way as before using Gephi, and its algorithms for finding communities and laying out the network. The sizing of the nodes gives an idea of their level of influence in the network, and its based upon their eigenvector centrality, which takes into account both how many times they were retweeted and who they were retweeted by. This means that retweets from individuals who were in turn retweeted themselves a lot are viewed as more important, which makes sense. So, the nodes that stand out here are natalieben, former leader of the Greenparty, Greens4anaimals which is the Greenparty group for animals, AnneekaSvenska an animal rights campaigner/TV presenter and a campaigner at Friends of the Earth (Gusyshrubsole). So, the most influential users seem to be those who are already involved in politics and campaigning. Each of these influential nodes is located at the centre of a cluster of nodes which have been identified as making up a community. Lots of the nodes in these clusters, particularly the blue, red and green ones have links only with the large node at the centre, meaning those individuals only retweeted the one user. This tells us that there are sub communities of interaction within the larger conversation, and that those within these might not be aware of the wider conversation taking place
- Classification of each user as either anti-grouse shooting or pro-grouse shooting based on their User Bio, results in 853 anti-grouse shooting, 146 pro-grouse shooting and 1,705 unknown Homophily: "love of the same" is the tendency of individuals to associate and bond with similar others. The Quadratic Assignment Procedure (QAP): Firstly, the correlation between the adjacency matrix for user-characteristics and user-interaction was calculated. To determine whether the correlation is higher than we would expect by chance, the QAP uses a non-parametric permutation to permutate the rows and columns of the user interaction matrix and calculate the correlation for each permutation. Repeating permutations 5000 times results in a distribution of correlation coefficients against which the correlation for the actual social network is compared to decide whether there is a significant homophily effect.
- The first questions asks: What can Twitter conversations tell us about the extent to which individuals engage in the petition process beyond the signing stage? E-petitions are intended to comprise the first stage in the process of raising the importance of an issue with government. However, some concerns have been expressed that the process of signing an e-petition is so simple that it may not be enough to really engage anyone. Some have also labelled e-petitions as “slacktivism”, that is to say that petitions provide a lazy way for people to feel they have taken action, without them really having to do anything. This is a concern if individuals who sign a petition feel their moral duty is complete and that this may cause them to not take part in another form of political action.
- Moving on from the first question which was making comparisons between different petitions, for the rest of the analysis I’m going to present to you today, I focused on one particular petition as this allowed for more in depth study and exploration of what it was possible to discover from the data. And the petition we focused on was this one, which was calling for a ban to driven grouse shooting and which received over 120,000 signatures. For those of you that don’t know – grouse are a kind of game bird, which look a bit like this and driven grouse shooting, unsurprisingly, involves shooting grouse. The grouse shooting takes place on moorland and to allow this it is managed very intensively and this has been linked to various environmental issues such as increased risk of flooding and greenhouse gas emissions, and additionally often leads to the deaths of animals such as foxes and mountain hares and the illegal killing of birds of prey. However, this is not a one sided debate as those people who enjoy the sport are very keen to protect it. This is a good choice for study as this petition was given an Oral Evidence session allowing the creator of the petition, amongst others to present more evidence. This allows us the chance to contrast the reaction on Twitter between the two.
- One method for extracting these topics is through consideration of text as a network, with the words making up the nodes of the network and the connections between these nodes representing a relationship between two words. In this network analysis the semantic meaning of the words, so what the word means, is discarded and relationships between words are indicated purely by their structural proximity to one another, that is how close they are to each other in the text. This is based on the idea that words that occur in similar contexts tend to have similar meanings. To produce a network from text, the first stage is to construct an edgelist, like this, detailing the number of times particular words occur together. Words may only be considered to co-occur if they are found next to each other, or a wider context for co-occurence may be considered such as a sentence or a block of a certain number of words. Preprocessing stage… A weighting is then given to each word co-occurrence based on how many times it was found in the text. .
- Related to this, the cluster in red containing “uplands, peat, dismissed, importance and so on” displays reactions to the fact that Caroline Lucas speaks up in the debate to highlight how grouse shooting damages upland peat and the impacts this can have on climate change, but is largely ignored.
- Then, there is a cluster in green relating to those supporting the ban, containing various twitter handles of campaigners and campaign groups, such as animalaid and league against cruel sports as well as Mark Avery, the creator of the petition and Chris Packham a BBC wildlife presenter who has spoken out in support of the ban. There is negative words around these names, such as directed, attack and vitriol.
- So these are filtered out and we’re left with a list of all of the words that convey sentiment. By multiplying their frequency and their sentiment score we can get a value which represents their overall contribution to the sentiment of the conversation. From this table it’s possible to create a graph which allows us to get a quick overview of how positive or negative a piece of text is.
- Like this, detailing pairs of users and the number of times they interacted. On twitter there are several ways in which you can interact with other users, but here we’ve based interactions based upon retweets, so in which one user reposts a post made by another user.
- We therefore construct random networks in which the structure stays the same, but the labelling of the nodes changes. So, in a simplified example this would be like this. We then repeat this a certain number of times, in my case 5,000 until we’ve produced a distribution of correlations for random networks against which we can compare the observed network. From doing this, I discovered that the level of correlation between grouse shooting status and interaction was higher than the level of correlation in a random network 100% of the time. From this we can conclude that the grouse shooting Twitter conversation definitely exhibits homophily.