Tag Archives: twitter

The Twitter Experience

For all the structuring effects of the Twitter functional features, the Twitter experience is generally perceived as a private one as only the individual user can see their Twitter feed, as they have structured it, on their particular screen configuration (Gillen and Merchant 2013). This aspect of the individualisation and heterogeneity of public and open textual communication adds to the complexities of interpreting, analysing and making sense of Twitter. Gillen and Merchant’s (2013) discussion of the capacity of Twitter users to organise the flow of discourses they are presented seems to ignore both the algorithmic impositions of, for example, Trending terms in that interface as well as the effects of the content of individual Tweets being perceived as a coherent informational flow or a chaotic mess of impressions (or both). The Twitter user experience is not an isolated or individualised one but is, rather, an entanglement of heterogeneous intentions, business logics, coded protocols, algorithmic outputs, collective norms and individual perceptions.

It is this entanglement between the human and material that opens, closes and patterns or orders the particular uses of Twitter. Twitter is constantly and actively made and remade in the intra-actions of user behaviours, hardware, coding, algorithms and visual design, rather than Twitter being a neutral utility or passive instrument.

Open online spaces of professional learning: searching for understanding  the ‘material’ of Twitter discussion events

Here are the slides from my presentation to the Social Informatics cluster group meeting of 13 June 2014.


Recent years have seen a growth in micro-blogging discussion events intended to support professional learning (McCulloch, et al, 2011; Bingham and Conner, 2010) communities. These events often take place on Twitter and are open to anyone using that service. The synchronous events are organised through the convention of hashtags (#) in combination with a shortened name as an explicit mechanism to aggregate contributions and enable open interactions (Bruns 2011).
This presentation will explore an initial investigation of two of these Twitter discussion event communities that both target corporate learning and development professionals. The overall study is concerned with how social discourses within a specific context emerge as sense-making and legitimation strategies around particular practices (Phillips and Hardy 2002: 25) and so will employ a multi-modal discourse analysis approach (Levine and Scullion 2004). However, the data from these Twitter discussion events does not have a transparently coherent structure as discussion sequences run coterminously and interrupt one another (Honeycutt and Herring 2009). So, with the purpose of “making sense of the data”, this presentation outlines the approaches used in identifying and analysing the key patterns of participation and structures of the Twitter discussion events. The descriptive statistical approaches suggested by Bruns (2014) are used to analyse the Twitter events and to discuss the limits of such analysis with reference to recent debates on the nature and status of ‘data’ in digital research (boyd and Crawford 2012; Baym 2013). The extent to which this kind of analysis can reveal the power and participation strategies of Twitter users in these events will be discussed.

Digital Scholarship day of ideas: data [2]

This is the second session of the day I wanted to note in detailed (the first is here). The session it Robert Procter on Big Data and the sociological imagination, Professor of social informatics at the University of Warwick. These notes are written live from the live stream. So here we go:

The title has changed to Big Data and the Co-Production of Social Scientific Knowledge. The talk will explain a bit more on social informatics as a hybrid computer scientist and sociologist; the meaning of ‘big data’ and how academic sociology can use such data including the development of new tools and methods of inquiry – see COSMOS – and concluding with remarks how these elements may combine in an exciting understanding of how social science and technology may emerge through different stakeholders including crow-sourced approach.

Social informatics is inter-disciplinary study of factors that shape adoption of ICT and the social shaping of technology. Processes of innovation involving districted technologies are large in scale and involve diverse range of publics such as understanding social media as processes of large-scale social learning. Asking how social media works and how people can use it to further their aims. As it is public and involves social media makes it easier in many ways to see what is going on as the technology makes much of the data available (although its not entirely straightforward).

Social media is Rob’s primary area of interest. Recent research includes on the use of social media in scholarly communications to put research in the public domain. But the value of this is not entirely clear. Identified positive and negative view points. The research also looked at how academic publishers were responding to such changes in scholarly communications such as supporting the use of social media as well as developing tools to trace and aggregate the use of research data. This showed mixed results.

Another research project was on the use of Twitter use during the 2012 riots in England in conjunction with The Guardian. In particular, was social media important in spreading false information during such events. So the research looked at particular rumours identified in the corpus of Tweets. So how do rumours emerge in social media and how do people behave and respond to such rumours?

This leads to the question of how to analyse 2.5m Tweets which is qualitative data. Research needs to seek out structures and patterns to focus scarce human resources for closer analysis of the Tweets.

Savage and Burrows (2007) on empirical sociology arguing that the best sociology is being done by the commercial sector as they have access to data. Academic sociology becoming irrelevant. However, newer sources of data that provides for enhanced relevance of academic sociology and this is reinforced by the rise of open data initiatives. So we can feel more confident on the future of academic sociology.

But how this data is being used raises further issues such as linking mood in social media with stock market movements but this confuses correlation and causation. Other analysis has focused on challenges to dictatorial regimes and the promotion of democracy and political changes and for social movements to self-organise. Methodological challenges are concerned with dealing with the volume of data so combining computation tools with sociological sensitivity and understanding of the world. But many sociologists are wary of the ‘computational turn’.

Returning to the England riots looking at the rumour of rioters attacking a children’s hospital in Birmingham. This involves an interpretive piece of work focused on data that may provide useful and interesting results. So the rumour started with people reporting police congregating at the hospital and so people inferred that the hospital was under threat. The computational component was to discover a useful structure in the data using sentiment and topic analysis – divided Tweets into original and retweets that combine in to an information flows and some flows are bigger than others. Taking size of the information flow as an indicator of significance can provide an indication for where to focus the analysis. Used coding frames to capture the relevant ways people were responding to the information including accepting and challenging Tweets. This coding was used to visualise how information flows through Twitter. The rumour was initially framed as a possibility but mushroomed and different threads of the rumour emerged. The rumour initially spreads without challenge but later people began to Tweet alternative explanations for the police being her the hospital i.e., a police station is next to the hospital. So rumours do not go unchallenged and people apply common-sense reasoning to rumours. While rumours grow quickly in social media but the crowd sourcing effects of social media help in establishing what the likely truth is. This could be further enhanced through engagement from trusted sources such as news organisations or the police? This could be augmented by computational work to help address such rumour flows (see Pheme).

There is also the question of what the police were doing on Twitter at the time. In Manchester, accounts were created to disseminate what was happening and draw attention to events to the police so acting to inform public services.

This research indicates innovation as a co-production. People collective experimenting and discovering the limitations and benefits of social media. Uses of social media are emergent and shaped through exploration.

On to the development of tools for sociologists to analyse ‘big’ social data including COSMOS to help interrogate large social media data. This also involves linking social media data with other data sets [and so links to the open data]. So COSMOS assists in forging interdisciplinary working between sociologists and computing scientists, provide interoperable analysis tools and evolve capabilities for analysis. In particular, points to the issues of the black-boxing of computational analysis and COSMOS aims to make the computational processes as transparent as possible.
COSMOS tools include text analysis and social network analysis linked to other data sets. A couple of experimental tools are being developed on geolocation and on topic identification and clustering around related words. COSMOS research looking at social media and civil society; hate speech and social media, citizen science, crime sensing; suicide clusters and social media; and the BBC and tweeting the olympics. Points to an educational need for people to understand the public nature of social media especially in relation to hate speech.

Social media as digital agora, on the role of social media in developing civil society and social resilience through sharing information, holding institutions to account, inter-subjective sense-making, cohesion and so forth.

Sociology beyond the academy and the co-production of scientific knowledge. Points to examples such as the Channel 4 fact checker as an example of wider data awareness and understanding and citizen journalism mobilises people to document and disseminate what is going on in the world. Also gives the example of sousveillance of the police as a counter to the rise of the surveillance state. The Guardian’s use of volunteers to analyse MP expenses. So ‘the crowd’ is involved in social science through collecting and analysing data and so sociology is spanning the academy and so boundaries of the academy are becoming more porous. These developments create an opportunity to realise a ‘public sociology’ (Burawoy 2005) but this requires greater facilitation from the academy through engaging with diverse stakeholders, provision of tools, new forms of scholarly communication, training and capacity building and developing more open dialogues on research problems. Points to public lab and hackathons as means for people to engage with and do (social) science themselves.

Twitter “ain’t all that”

A useful reminder that Twitter Should Not Be Your Only Communications Channel in organising and promoting events for the following reasons:

Firstly, not everyone is on Twitter. Wouldn’t have thought this point needed made, but apparently it does.

Secondly, not everyone follows the right people on Twitter. This applies doubly if you are organising an event and tweet details from your personal account and not some kind of event account. How vain are you to assume that everyone who matters to your event follows your personal account?

Thirdly, even if they do it’s very easy to miss a Tweet. If you don’t check Twitter regularly it’s easy to miss old Tweets, especially as they show new Tweets first.

And even if you do see a Tweet going past containing a fact you need to remember it’s to easy for it to slip past without you having recorded it, and next time you try to look for it it’s almost impossible to find. (eg “Where is tonight’s event? I know someone tweeted it last week but now I can’t find it!”)

Now we get on the two way communication part. Again, there’s nothing wrong with doing this – the problem comes if you only do this and make no other communication channels open.

Firstly, it’s 140 characters. You can’t discuss any details, or any points of finesse, or a complex situation. You just can’t. Communication is superficial.

Secondly, almost all communication is public and many people aren’t happy with that. Maybe the nature of their comment means they want to discuss it in private?

And lastly, remember that for large segments of the population, Twitter is not a safe spaceNot in the slightestReally not. If someone does not feel comfortable using Twitter, are you happy excluding them from your communications, remembering that they may already feel excluded from many other things already?

A model of discussion events on Twitter

As previously discussed here & here, I am studying two Twitter discussion events as sites of professional identity formation and development. The broad structure of the two events is broadly similar to the research process of a Tweetstorm: “an online, open brainstorm-like session via Twitter” (Sie, Bitter-Rijpkema, and Sloep 2009: 60). A Tweetstorm was described as a six stage process involving: (i) the context established by, for example, a topic briefing; (ii) questions are presented on Twitter by the event moderator organised using the specified event hashtag; (iii) answers to the questions are given as tweets by participants; (iv) these tweets are aggregated, for example, using Tweet Archivist; (v) the aggregated tweets are analysed into categories and (vi) the categories are then analysed. The outputs from a Tweetstorm are a series of core statements drawn from the knowledge of the participating experts. As such, a Tweetstorm has similarities to the processes of Delphi studies (Nworie 2011) or collaborative concept mapping (Simone, Schmid, and McEwen 2001).

The individual discussion events broadly followed the structure of a Tweetstorm. However, in these discussion events, the Tweets are not aggregated, categorised or systematically analysed. Rather, they conclude with a call for participants to identify the key points of the discussions and any actions they may take in response to the points made.

Based on the notion of the Tweetstorm, the chat events’ structure can be summarised as follows:

Figure 1: structure of the chat events

Figure 1 Sie et al



Nworie, John. 2011. “Using the Delphi Technique in Educational Technology Research.” TechTrends 55 (5) (August 11): 24–30. doi:10.1007/s11528-011-0524-6.

Sie, Rory, Nino Pataraia, Eleni Boursinou, Kamakshi Rajagopal, Isobel Falconer, Marlies Bitter-rijpkema, Allison Littlejohn, and B Peter. 2013. “Goals , Motivation for , and Outcomes of Personal Learning through Networks: Results of a Tweetstorm.” Educational Technology & Society 16 (3): 59–75.

Simone, Christina De, Richard F. Schmid, and Laura A. McEwen. 2001. “Supporting the Learning Process with Collaborative Concept Mapping Using Computer-Based Communication Tools and Processes.” Educational Research and Evaluation 7 (2-3) (September 1): 263–283. doi:10.1076/edre.

Social Network Analysis and Digital Data Analysis

Notes on a presentation by Pablo Paredes. The abstract for the seminar is:

This presentation will be about how to make social network analysis from social media services such as Facebook and Twitter. Although traditional SNA packages are able to analyse data from any source, the volume of data from these new services can make convenient the use of additional technologies. The case in the presentation will be about a study of the degrees of distance on Twitter, considering different steps as making use of streaming API, filtering and computing results.

The presentation is drawn from the paper: Fabrega, J. Paredes, P. (2013) Social Contagion and Cascade behaviours on Twitter. Information 4/2: 171-181.

These are my brief and partial notes on the seminar taken live (so “typos ahead!”).

Looking at gathering data from social network sites and on a research project on contagion in digital data.

Data access requires knowledge of the APIs for each platform but Apigee details the APIs of most social networks (although as an intermediary, this may lead to further issues in interfacing different software tools, e.g., Python tool kits may assist in accessing APIs directly rather than through Apigee). In their research, Twitter data was extracted using Python tools such as Tweepy (calls to Twitter) and NetworkX (a Python library for SNA) along with additional libraries including Apigee. These tools allow the investigation of different forms of SNA beyond ego-centric analysis.

Pablo presented a network diagram from Twitter using NodeXL as ego-networks but direct access to Twitter API would give more options in alternative network analysis . Diffusion of information on Twitter was not possible on NodeXL.

Used three degrees of influence theory from Christakes & Fowler 2008. Social influence diffuses to three degrees but not beyond due to noisy communication and technology/ time issues leading to information decay. For example, most RTs take place within 48 hrs so tends not to extend beyond a friends, friends friend! This relates to network instability and loss of interest from users beyond three degrees alongside increasing information competition as too intense beyond three degrees to diffusion decomposes.

The  direct research found a 3-5% RT rate in diffusion of a single Tweet. RT rates were higher with the use of a hashtag and correlate to the number of followers of the originator but negatively correlates to @_mentions in the original Tweet. This is possibly as a result of @_mentions being seen as a private conversations. Overall, less than 1% of RTs went beyond three degrees.

Conclusion is that diffusion in digital networks is similar to that found in physical networks which implies that there are human barriers to communication in online spaces. But the research is limited due to the limits on access to Twitter API as well as privacy policies on Twitter API. Replicability becomes very difficult as a result and this issue is compounded as API versions change and so software libraries and tools no longer work or no longer work in the same way. Worth noting that there is no way of knowing how Twitter samples the 1% of Tweets provided through the API. Therefore, there is a need to access 100% of the Twitter data to provide a clear baseline for understanding Twitter samples and justify the network boundaries.

Points to importance that were writing code using R/ Python preferable as easier to learn and with larger support communities.

Weeknotes [21022014]

A picture of various draft word processed documents
In this last week, I have mainly been:

Twitter and Micro-blogging conference – third and last day

These are notes from the Twitter and Micro-blogging conference at Lancaster University for day 3.  The full programme can be found on Lanyard.The Twitter hastag is #LUTwit

I am also following eLearning @ Edinburgh on #elearninged

First session is Cracking the Code on Twitter use by media fandom by Rhiannon Bury at Athabasca Uni.

Places her research in the context of the increase in Twitter users representing 16% Americans as Twitter users compared to 67% using Facebook. Using a survey of TV fans found younger fans using twitter more frequently than older fans and female fans more likely to use Twitter than male fans.

Interested in micro-celebrity and presentation of self (Goffman). But pointing to lack of analytical research of Twitter use as a shared system of meaning and the semiotics of Twitter at the micro level of the Tweet and the macro level of the feed. For this presentation, focusing on the macro level and the aggregation through the feed (although could look to the hashtag).

Looking at syntagmatic relations of signs as linearity, combination, addition and deletion; and paradigmatic concerned with selection, substitution and intratextual relations (Chandler 2002). Also interest in connotation as in link between sign and the user – combining system and use in analysis.

At micro-level, Tweet as syntagma of text as speech act and visual structured temporally in the feed given as the date/ time stamp. [but temporal structure more complex than that – impact of the (delayed) RT – parallel temporal frames].

At macro-level and aggregation becomes complex. Feed has only one structure: newest to older and [symmetry] as software forces importance of the latest Tweet.

Access to twitterverse is always partial, incomplete and fragmented – can’t see all 500m users at one time.

Understanding fan Tweets as secondary text – primary text being the TV show. With fans, text is never just informational but also affective – pleasures of the text and signify importance of fan. Found that fans tend to follow and read but little original content generation and an emotional attachment. Twitter affords a feeling of a closer relation to producers, celebrities, etc.


Now going on to session on political agenda setting in Belgium elections by Pieter Verdegem.

Presenting paper with two disclaimers that alot of the data analysis by his PhD student @eveliendh and that this is the first run of analysis being presented here.

Points to problems of using Twitter to predict election results and paper titled I Wanted to Predict Elections with Twitter and all I got was this Lousy Paper. But his own analysis looks to issues of influence and potential disintermediation of traditional media and impacting on behaviour of politicians on SoMe. Cites concept of liquid politician. Three groups of stakeholders: politicians, media and citizens

Belgium as country with the longest period with no government. Harvested Tweets of local elections 2012 (municipalities) but some candidates also standing/ sitting as Federal politicians. Saw a typical pattern of Tweeting with massive spike of Tweets in the few days around the election day (see Bruns).

45K Tweets with 42k on election day. Completed some content analysis to identify the ‘loudest’ voices which were mainly established and institutionalised accounts – parties, traditional media etc.

Green Party Twitter account was one of the most active with parties receiving more while citizens stronger in sending Tweets.

Hyperlinks used especially to traditional media outlets but also to new media (blogs, YouTube etc.). Analysed use of additional # to the formal election # to emphasise parties, localities and humour

Tweeting showed a flat and decentralised network with increased in interaction following election much higher than expected.

Responses were interesting: citizens more likely to repsond to questions from institutions than other citizens … while institutions rarely responded to citizen questions.

Intending to do much more research on interactions, content analysis and SNA and comparison with other elections and with other periods of Twitter activities (non-elections).


On to the final plenary by Ruth Page on Saying Sorry: corporate apologies posted to Twitter. 

States a certain ambivalence about going last but gets to have “the last say”.

Why Twitter is significant for corporations? Twitter as a participatory environment (Jenkins 2006) without gatekeepers giving unmediated access. But Twitter not an even playing field and is part of the rest of SoMe and ‘real world’ interactions with the similar power relations of social practices. But also on how Twitter is used and the affordances of Twitter platform.

Jansen (2009)  Twitter as e-word of mouth. Is useful for orgs to follow and track. 51% Twitter users follow corporate accounts in respect of corporate new and/ or customer care

The data set: 17.7k tweets; 100 accounts; 40 companies; 30 celebs; 30 ordinary accounts gathered 2010 and 2012.

Research objective on apologies emerged rather than preplanned – identified 1200 tweets with apologies in them.

Distinguishes between tweet types updates (one to many ‘broadcasts), public but addressed to individual and RT – doesn’t take in to account use of quotes or MTs. All types accounts had more updates than other types – especially for corporates.

Corporate broadcast tweets involving pushing, brand and across link analysis

Corporate brand promotion using hashtags used in updates (one to many) and hashtagging increasing over time even tho # not needed to be included in revised twitter search algorithm. Corps tending to use hashtags of their names, products or area of expertise so linked to brand positioning. Ordinary peoples’ hashtags tend to position them as consumers and interactors. Noting dichotomy between corp branding and ordinary ppls’ positioning as consumer/ audience.

Rise of ‘amplified talk’ in terms of including hyperlinks in tweets – positioning selves as authoritative recommenders of sites/ resources but this is changing now as more people are sharing links increasingly across multiple platforms. Also seeing collapse of the division between personal and professional between 2010/12. For corps tend to link to own websites and customer engagement platforms, eg, flickr

Modified RTs has stronger growth 2010 – 2012, especially for purposes of self-promotion eg, customer endorsement.

By 2012 corps increasingly using address messages, and modified RTs suggesting an increasingly interactional/ conversational use of Twitter. Used corpus linguistics for analysis of tweets and found pattern of customer care talk as increasingly prominent in corp tweeting.

Now showing a clip from Big Bang Theory

Approaches to apologies: apologies as reluctant; or to music (!) and political apologies (see Nick Clegg).

Apologies are ‘post event speech acts to enable future interaction and restoration of equilibrium. Linguistic research on apologies is very rich but mostly private and spoken apologies. Much less research on public apologies.

Have identified in research five compents of apologies: using term sorry; taking responsibility; offer to repair the offense; promise to avoid it repeating. But in twiter corps not take responsibility or promise not to happen again (accountability, power to take responsibility and legel implications of promising not to repeat the error – implied liability).  Corps tend to not restate what the actual problem is … as need to acknowledge that they’re dealing with an individual without broadcasting the problem.

Only 10% of corp apologies include an explanation and where did so was to: down play their responsibility, eg, customer is wrong; blaming a third party and factors beyond the company’s control (weather). Avoid suggesting direct agency of the company (eg, caused by office closure… not “we closed the office”)

30% corps make offer of repair (compared to 10% ordinary users) – corp repairs around refunds; investigation etc.. fixed by others not the tweeter illustrating the interactional context as draws in wider corp resources.

Corp apologies also include an imperative – telling the customer to do something – wait for corp reaction or asking customer to do something, eg, please email us … but imperatives are risky for the corp as don’t close the loop/ resolve the complaint.

Also noted that corps tend to start tweets with “Hi” (19% of corp apologies – to personalise response but also shows social distance and not really knowing the individual) and close with a “thanks” and signature (37% of apologies). Ordinary people are more conversational and informal in apologies.

Emoticons (oh dear) used to mark alignment between corp and customer but can be used to mitigate a negative response (smiley face for stating can’t do anything) or to enhance the interaction (eg, following).

Patterns: avoid reputational damage – avoid restating problem; deny agency; signify social distance; avoid explanation or if do, then seek to repair.

But so what? Interesting for a linguist but challenge for the speaker is to make this research useful? Looking t potential for this analysis for customer care training and PR impacts.

Twitter and micro blogging – presentation slides

My slides on Discursive practices in informal learning events on Twitter presented at #LUtwit:

Twitter and micro-blogging notes on day 2

These are notes from the Twitter and Micro-blogging conference at Lancaster University for day 2.  The full programme can be found on Lanyard.The Twitter hastag is #LUTwit

Conceptualising Twitter as a discourse system by @mdanganh

Looked at the Function of the # – lead to theory of contextualisation based on John J Gumperz conversational inference and contextualisation cues as surface feature that are verbal and non-verbal. So can be used to understand and analyse #

Cues reconfigure conversational contexts that presuppose and create context as social ordering (Bruns & Burgess 2011).

Key part of Twitter as a discourse system. Identifies four functional operators in Twitter: the RT; the @; the # and the link That have technical and communicative function as well as positioning Twitter as intertextual and interdiscursive

For data drawn from Federal State elections 2010 – 2013 over a four week period each year from parties, media, politicians, public interactions, #. Analysis uses

– profile analysis (quant)

– speech act analysis (qual and quant) (Searle), eg, inform, state, assert, announce, request etc……. Found predominately speech acts concerned with exchanging information, especially from the institutional accounts

– discourse analysis (quant informed qualitative analysis

Use case of Conservative candidate #Rottgen. But lost NRW State election and subsequntly also dismissed as Federal minister by Angela Merkel (as a ‘mother’ figure). Discourse developed as mother metaphor

# frames Tweets in to a story narrative frame that is emergent and the co-construction of meaning.


Now on to the plenary session with @GregMyers on Working and Playing on Science Twitter

First Tweet on an April Fools as example of different types of Twitter streams – such as different communities  or genres. @GregMyers on writing on blogging realised that there is not one ‘thing’ of a blog – share a media but are very different. Are we talking about one genre or not? Looking at the different papers at the conference it is clear that there is not a single genre or function.

How do different Twitter communities use Twitter? Are there genre differences. Focus here on science Twitter of research scientists.

Networking is a part of any science project from the 16 century onwards. But as a community, depend for reward on the production of a very different text object, the published paper which is very unlike Twitter. So science community is a network of texts but also involving equipment, people, methods, money (ANT).

Identified two themes of sociology of science:

1. heterogeneirty of scientific networks: ANT. You become powerful in science by maintaining a network

2. rhetorical tension between empiricist repertoire as timeless claims in the formal literature and a contingent repertoire and time bound and contingent activities.

Cites letter from C19 that is very Twitter like albeit as provate letter rather than a public Tweet.

More information on Greg’s blog: http://thelanguageofblogs.typepad.com

Corpus analysis based on keywords eg, paper, scientist, research, etc… but more interesting keywords such as: over use of “i” (compared to other Tweets) as a sign of formality; use of “of” as signifier of more complex; “but” as academic signifier and a negative keyword of “love” as evaluation.

Gives ground to identify scientists as a distinct community on Twitter.

Gives an example of phatic communication – communication for the sake of contact (“who is still working” at 3 am). Problematises the use of the term “here” as “a lab” rather than a geographic co-location. Solidarity building?

Particular interest in references to time: current time – what I’m doing now; temporal cycles of, eg, work , publications, terms; future time (what will be happening); and chunking time eg, pleistocene.

Gives example of scientific criticism and never-ending use of citations and references but also criticism of socio-thermodynamics using LOLcats

Scientific criticism involves personal stance; impersonal references to shared norms and hierarchies of authority for presentational purposes. Found many Tweets involve boundary work, sealing off science and non-science while at the same time concerned with outreach and public engagement with science.

Good set of question of a Twitter community:

  • present self as a community?
  • make a distinct genre – eg, use of RT, links etc…?
  • use the same genre / register?
  • how Twitter practices relate to other practices?
  • what specific kinds of performance are valued?
  • how permeable are the boundaries of the community? How many Tweets get RT from outside the community?

Permeability of the science community enhanced as scientist may be member of other communities that may cross-overs of their specific Tweets (hip hop, feminism/ women in science). But not seeing non-scientists coming back and commenting on scientific discussions.


The afternoon session is about to kick-off with Noreen Dunnett on The Tweeting Zone with Twitter providing a mechanism for renegotiating boundaries between Activity Systems. Looking also at how Twitter allows renegotiation of identities and roles of learners and teachers in formal learning spaces.

Referes to liminal spaces as a rite of passage in which a person moves from one state of being to another. Could Twitter affordances at act bridge between Activity Systemas a a boundary zone between different systems and spaces? Does Twitter provide scaffolding between learning and working definitions.

Affordances (actionable properties …. user perceives some action is possible. Gibson 1977, 1979 and Norman 2004). The paper uses Connectivism and Activity Theory examining a teacher training course and the student use of Twitter ordered around a given #

Frames Twitter in terms of a Personal Learning Environment (PLE) as allowing learners to coordinate arrangements between people, materials and technology so the PLE is not a platform but is rather a process that requires agency from the learner [as actor].

Uses ethnographic action research including participant observation, interview and survey. Observation of a Twitter chat over a seven month period with researcher moderating initial discussion. Spaces of learning in, eg, Twitter, enacted in to being – emerge rather that design/ predetermined (Al Mahmood 2008).

Screen shot 2013-04-11 at 13.54.07

Cited example of student who left the course asking for permission to continue to use the hashtag.

Trainee teachers participate in a range of discourse communities simultaneously, spanning formal and informal learning environment. The course tutor conflicted about Twitter and the degree of control and policing role.

Useful Tweet here:

Screen shot 2013-04-11 at 13.45.30

Twitter bridge Activity Systems as discourse communities.

Role of tutor not clear: has emergent and non-emergent elements as the Twitter space was formally set up to support students in placement but tutor also wanted to use it for learning tasks by setting up a series of tasks Tutor was concerned that the students controlled by eg, GTC notions of professional conversation.

References from the presentation can be found here


Now out of power and seeking a plug point ……


Now at An analysis of professional exchange and community dynamics of Twitter from Nicola Osborne and Clare Llewellyn from Edinburgh

Used Martin Hawksey’s TAGs for grabbing #feeds into Google Docs.

Social media collaboration group came into being to analyse Tweets on London riots using manual analysis and wanted to use more automation in the analysis. Clare developed a prototype as the JISC Twitter Workbench initially for analysis of Olympics on Twitter  but extended in to more general academic use. Currently working on developing the Workbench to work with smaller, discreet data including elimination of direct (unchanged) RTs. Testing Workbench for use at a conference (done aferwards but could be done in real time).

Used algorithm  for LDA clustering but found it no more accurate than incremental clustering 

At the conference, was a lot of interest in, for example, PechaKucha and specific talks that gained a lot of interest.

Found different algorithms appropriate for different size of event/ Twitter hashtags. Clusters confirmed some hunches about the conference.

Noted that clustering does not analyse influence of a Tweet. Confirmed participant feedback on conference sessions. Did identify what was popular (not influential?) and ‘hot topics’ etc which could have been very useful for real time use eg, in back channel. Could imply unpopular sessions not Tweeted but this is not clear.

Very clear decline in volume of Tweets after conference – often sharing links.

Analysis was about the content of Tweets and not about connections between Tweets…

Balance to be developed between clustering duplication versus clustering granularity.

Q of why JISC funded this given the existence of NodeXL

Now time for a break….

Fell behind on the blogging – lack of power, fat fingers etc….

Now at the plenary Professional Twitter Panel which can be followed at #lutwitrc

Discussing finding the time for Twitter and intensity, @johnnyunger very variable in intensity of Tweeting. Mentions that avoiding marking leads to increase in Twitter use.

A number of comments in Tweeting in between times

Screen shot 2013-04-11 at 16.25.01

Tweet when we’re doing other things or when can’t do other things

But also comments about the rhythms of the day – energy, roles etc…

Screen shot 2013-04-11 at 16.28.00

… and in relation to activities in “real life” (sitting on the bus) as well as on other SoMe.

Discussion on whether Twitter is distracting or takes time [but avoids issues of cognitive-shifting]

Moving to ethics, eg, is it OK to be anonymous on Twitter and issue of institutional constraints. Also scales of anonymity, eg, less easy to identify the individual rather than anonymous per se.

Comment on analysis of HE SoMe policies that are very constraining requiring various disclaimers for staff. HE senior management prickliness on potential reputation harm from ‘rogue staff’.

Comment that first rule of the internet that there is no such thing as anonymity – don’t say something online you wouldn’t say elsewhere (from @pennyb).

Moved on to impact – beyond simply number of followers but also who follows.

Discussion on ethics and the nature of public domain with good understanding of the nuances around anonymising Tweets. Also refering to Twitter TOS in tension with research ethics, eg, on anonymity.