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.
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.