1. Twitter conversations about petitions vary greatly in the level of engagement beyond just signing, from a few tweets to thousands, suggesting people sign petitions as a form of slacktivism or to genuinely engage in the issue.
2. Analysis of tweets about the grouse shooting petition found discussions of the positives and negatives of hunting, and criticism of the polarized parliamentary debate as favoring one side.
3. Those tweeting about petitions interact in closed communities with those they already agree with, indicating conversations reinforce existing views rather than consider different perspectives.
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The #transneeds Listening Campaign was launched in September 2015 by a volunteer team to address the lack of data on the needs of transgender people. They conducted a social media listening campaign using Twitter and SMS to anonymously collect over 12,000 messages identifying concerns. The key issues raised included discriminatory barriers to basic needs like housing, healthcare, education, employment, and public accommodation. Respondents reported high rates of harassment, assault, poverty, unemployment, and suicide attempts. The team reported these findings to the White House with recommendations and showed that unconventional data collection through social media can help give voice to overlooked populations.
Analysing conversations on Twitter: Do e-petitions help to increase public e...Robyn_CDRC
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Using Twitter as a Postgraduate ResearcherSimon Bishop
Second version of my talk. I tried to make it more focused and a better introduction. As ever, cute pictures need no explanation.
As for Up - try explaining the plot of it to someone who hasn't seen it... ridiculous, isn't it? There's no way to sell it that way, they have to see it. In the same way, to describe how Twitter works gives no indication as to its functionality. You have to play with it and learn by experience.
Pew Internet Director Lee Rainie discussed the new media ecosystem with leaders of community foundations from Western states and several other locales. He described how three technology revolutions have made the media world personal, portable, participatory, and pervasive in people’s lives and how those changes have affected communities.
This document discusses a topic modeling analysis of tweets from members of the 113th US Congress. The analysis aimed to identify patterns in party messaging and how individual members adopted or diverged from party messages. Topic modeling of over 180,000 tweets from 522 members identified 40 topics. Results showed that while members discussed a wide range of issues, both Democratic and Republican party accounts focused more on a few key messaging topics. However, some members diverged from party stances on certain issues like ISIL and Keystone pipeline.
The #transneeds Listening Campaign was launched in September 2015 by a volunteer team to address the lack of data on the needs of transgender people. They conducted a social media listening campaign using Twitter and SMS to anonymously collect over 12,000 messages identifying concerns. The key issues raised included discriminatory barriers to basic needs like housing, healthcare, education, employment, and public accommodation. Respondents reported high rates of harassment, assault, poverty, unemployment, and suicide attempts. The team reported these findings to the White House with recommendations and showed that unconventional data collection through social media can help give voice to overlooked populations.
Analysing conversations on Twitter: Do e-petitions help to increase public e...Robyn_CDRC
This document discusses a pilot study analyzing Twitter conversations about various UK e-petitions to understand if the e-petition system increases public engagement with politics. Key findings include identifying topics and communities in Twitter debates about specific petitions using natural language processing and network analysis. The study aims to inform a larger ESRC grant proposal evaluating the UK Parliament's e-petition system.
The document discusses social media analysis and summarizes key findings from analyzing tweets related to UK politicians. It finds that abuse towards politicians on Twitter was more common in 2017 than 2015, and that a small number of prominent MPs received most abuse in 2015. While men received more abuse than women in 2015, the targets of abuse changed in the 2017 analysis.
Understanding the world with NLP: interactions between society, behaviour and...Diana Maynard
The document discusses analyzing social media data, particularly tweets, for natural language processing tasks. It provides examples of analyzing tweets to understand information sharing during disasters, monitor opinions in real-time, detect topics and analyze political discussions. It also discusses challenges in analyzing tweets like informal language, ambiguity and misleading contexts or hashtags. Precise information extraction and annotation of tweets is needed to accurately identify hate speech, abuse and analyze its targets and changes over time. A multi-step pipeline including collection, preprocessing, information extraction and classification is proposed to understand abuse toward politicians from tweets surrounding UK elections.
Strategic use of Twitter in Local Government: A Northern Ireland StudyUlster University
This paper presents the results of a survey of Twitter usage in Northern Ireland’s twenty-six councils. The data was gathered in Summer 2012. The research questions were developed from a review of the literature on use of social media by government and focused on the role of social media as a communication channel to local government, examining the dialogue between government and citizen and the sentiment of such dialogue. The results show significant heterogeneity in Twitter use amongst the councils; with many not engaging at all, while a small number were highly engaged with their citizens. Regardless of the perspectives of the councils, there was evidence that there was a demand from the citizens for conversations that was not being met by the councils. The paper recommends that councils need to define a social media strategy in order to maximise the use of social media, but reflects that the councils should find it easy to engage with citizens by simply asking them via Twitter.
Using Twitter as a Postgraduate ResearcherSimon Bishop
Second version of my talk. I tried to make it more focused and a better introduction. As ever, cute pictures need no explanation.
As for Up - try explaining the plot of it to someone who hasn't seen it... ridiculous, isn't it? There's no way to sell it that way, they have to see it. In the same way, to describe how Twitter works gives no indication as to its functionality. You have to play with it and learn by experience.
Pew Internet Director Lee Rainie discussed the new media ecosystem with leaders of community foundations from Western states and several other locales. He described how three technology revolutions have made the media world personal, portable, participatory, and pervasive in people’s lives and how those changes have affected communities.
Denver Event - 2013 - New Media Ecosystem: Personal. Portable. Participatory....KDMC
The document summarizes key findings from a Pew Research Center report on digital technology trends in the United States. It finds that broadband internet access at home has increased dramatically, with 66% of Americans now having broadband at home. Mobile internet access through smartphones and tablets is also widespread, with 56% owning smartphones. Social media usage has also increased significantly, with 61% of American adults now using some form of social media. The document concludes by discussing how digital technologies have networked both people and information, changing civic engagement and the flow of information.
Using language to save the world: interactions between society, behaviour and...Diana Maynard
The document discusses social media analysis and natural language processing as applied to Twitter data. It provides statistics on Twitter usage and the most followed accounts. It then discusses challenges in analyzing social media text due to informal language usage and outlines common NLP preprocessing steps. Applications discussed include identifying named entities, geotagging tweets, user and topic classification, and analyzing hate speech directed at politicians on Twitter around UK elections in 2015 and 2017.
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The document describes a research project that aims to improve citizen engagement with televised political debates. It involves developing new tools and methods for analyzing debates, harnessing audience feedback, and creating an open debate replay platform. The goal is to better understand audience reactions, promote informed participation, and enhance democratic discussion. Focus groups provided insights into citizens' perceptions and democratic values related to debates. Prototypes tested capturing feedback using flashcards during live debates. Future work includes expanding these methods and building open-source debate analytics and visualization tools.
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The workshop aims to:
1) Have participants fill out a questionnaire and watch a clip from the Scottish referendum debate to spark discussion.
2) Brainstorm design solutions for increasing civic engagement at the local level.
3) Quickly prototype paper designs for local engagement projects.
The document discusses digital communication strategies for social advocacy. It explains that effective strategies involve two-way and multi-directional communication using social networks and media to build communities. It provides tips for growing influence on social media, such as finding and engaging influencers, targeting government officials using tools like hashtags and petitions, and measuring engagement and reach using metrics. The goal is to maximize audience interaction and drive traffic to further advocacy efforts.
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This document analyzes tweets from the #ausvotes hashtag during the 2010 Australian federal election. It finds that 415,009 tweets were posted by 36,287 users from July 17 to August 24, 2010. Discussion of political topics increased in the later part of the campaign. Key themes included the National Broadband Network, internet filtering, climate change, and asylum seekers. Politicians dominated retweets and replies, while journalists received more engagement. The #ausvotes community discussed events in real-time but also had its own interests and frames of reference.
This document discusses research on transgender communities on Tumblr. It begins by outlining the research questions around what can be learned from trans youth networks and how trans data can inform theory. It then describes the methods used to analyze over 1 million posts from the #ftm and #mtf hashtags on Tumblr. Key findings include Tumblr serving as an archive of experiences, a source of medical knowledge, and a site for cultural production and identity exploration. The document argues for an approach to trans data that recognizes its situated nature and holds binaries in tension. It suggests trans theory can benefit from understanding lived experiences and recognizing manifold identities.
This document discusses the use of Twitter during the 2008 US election campaign. It notes that Barack Obama's Twitter account became very popular and helped drive Twitter's growth. It also discusses how Twitter was used by various campaigns and politicians to engage voters, share announcements and updates, and mobilize supporters. The document concludes that candidates who engage voters on social media like Twitter have an advantage, and that political use of Twitter will likely continue increasing in importance going forward.
The document summarizes a study that examined discussions between Dutch MPs on Twitter to determine if they constituted a networked public sphere for political deliberation. Network and content analyses were conducted on over 7,000 tweets between 144 MPs. Results showed the network had low modularity and connectivity. Structural features of Twitter facilitated participation, but content analysis found only moderate evidence of rational debate between MPs, with limited justification and reflection in tweets. While individual outcomes could not be determined, collective deliberation may facilitate consensus and legitimate parliamentary decisions.
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Throughout the two days of the National Behaviour Change Congress my team of six social media scribes captures key speaker notes and insights as well as worked with participants to familiarise themselves with Twitter.
There were six Congress topics and tweets were arranged accordingly, further discussion took place with other behaviour change practitioners internationally and many participants signed up to Twitter for the first time.
The Harvest presentation was the final presentation given at the Congress and fed back to the group what had happened on social media including key barriers and opportunities for next time.
Talk I gave recently for some senior execs on getting started in social media. Why we share, what to share and how. Won't make so much sense without the commentary but hopefully some interesting slides...
The document discusses how the UK Parliament is building a knowledge graph to help solve problems related to managing large amounts of complex information. It provides background on what Parliament is and the challenges it faces with fragmented data across different departments and websites. It then covers how the Parliament is taking a domain-driven design approach to develop a knowledge graph, which involves modeling the key concepts and relationships within Parliament in a structured way. This includes developing ontologies around key domains like parliamentary procedure. It discusses some of the tools and visualizations that have been created so far to explore the knowledge graph, including maps of treaty procedures and tools to search for precedents.
A talk given to the IFLA Library and Research Services for Parliaments Section and IPU Joint Virtual Conference "Parliamentary library & research services – towards an agenda for the next decade"
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The document discusses digital communication strategies for social advocacy. It explains that effective strategies involve two-way and multi-directional communication using social networks and media to build communities. It provides tips for growing influence on social media, such as finding and engaging influencers, targeting government officials using tools like hashtags and petitions, and measuring engagement and reach using metrics. The goal is to maximize audience interaction and drive traffic to further advocacy efforts.
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What do Twitter conversations tell us about petitioning?
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…..?
19.
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
Editor's Notes
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.