Análisis externo

In this section and in the next I will revisit the research questions which guided this study, starting with the first research question (RQ1): How does freelance language teachers’ professional development on…and with…and through Twitter work?

The data vignettes presented in my thesis showed that FLTs’ Twitter-based PD involves a situated entanglement of human and non-human elements, which include FLTs’ teaching and tweeting practices. Tweet and hashtag assemblages move rhizomatically across and beyond Twitter, sometimes through the ‘strength of weak ties’ (Granovetter, 1973), i.e. through network actors who work as bridges between networks. Examples from my research include the actors Teacherentrepreneur and ELT_Teacher (linking the networks #ELTchat and #LTHEchat) and Heather (connecting the networks #ELTchat and #webconf2016). While #ELTchat’s Moderator1 and Moderator 3 also function as network links, these links were much more robust, due to their personal involvements in the #webconf2016 conference (Moderator1) and the #ELTwhiteboard network (Moderator3).

Some research participants associated PD on Twitter with notions of democracy and equality: “I guess the top thing is we're all on the same level. There are no names, titles, and if you're friendly polite and honest, you get to talk to some amazing people.” (Hanna, questionnaire). On the other hand, notions of ‘community’ were also present: “I feel like I'm part of a bigger whole” (C, questionnaire), “part of a larger community (H., questionnaire). The issue of trust also played a role in this context: ”It is possible

to create a group of people who you respect and trust in terms of professional skills and abilities and to learn from them.” (L., questionnaire)

However, social network analyses of the #LTHEchat and the #ELTchat network tweets showed a different picture with regard to equal participation. A comparison of the tweeting activity in both networks (see Appendix 7 and Appendix 9) over a period of four months (#ELTchat) and four weeks (#LTHEchat) weeks showed that a small group of network actors accounted for most tweets and retweets (out-degree) and were mostly referred to by other Twitter users (in-degree). This confirms the findings from a previous study, carried out by Rehm and Notten (2016): “Individual actors engage into creating and sustaining interpersonal ties. As a result, they are able to attain more central positions in the network. This in turn provides them with access to more and more diverse sources of information” (p. 221).

As argued in Chapter 4.4, Twitter metrics, such as the number of tweets and the number of followers constitute valuable information for Twitter and co-determine algorithmic workings. Appendix 10 shows that the number of tweets increased for each of the six research participants, whose tweets and retweets were collected, albeit at a different rate. Whereas Heather only sent 7 tweets in four weeks, Marc sent 491 tweets. The number of tweets and retweets per participant ranged from 17 (Heather) to 890 (Marc), as visible in Appendix 1. There was also a great difference in the number of followings and followers between participants, ranging from 72 followings/87 followers (Maria) to 2,300 followings/12,255 followers (Laura). Since visibility is connected to the number of followers, Maria’s tweets were much less visible on Twitter than Laura’s.

A higher tweet visibility facilitates the acquisition of new followers through one’s tweets, not least because tweet visibility is re-worked as tweet or hashtag

recommendations through Twitter’s algorithmic workings. These recommendations work with Twitter user recommendations, i.e. Twitter users recommend following other Twitter users, thereby attributing ‘importance’ to individual Twitter users.

Simultaneously, the number of followers is regarded as an indicator for a person’s influence or ‘importance’ on Twitter, leading to more followers and perpetuating the connection between follower number and presumed importance. The quantified ‘importance’ in turn feeds into Internet services that connect with Twitter’s API, which amplifies the effect beyond Twitter.

Comparing the number of followings/followers per participant shows a roughly equal increase (see Appendix 10), with the notable exceptions of Hanna and Laura. The increase in the number of Hanna’s followers (+25) is about double the increase in her own followings (+11), which could be attributed to her tweeting activity (410 tweets and retweets) during that time. However, the steep increase in the number of Laura’s followers ( +1,531) is puzzling. Asked about the increase in the number of her

followers over a short period of time, Laura revealed in her interview that a social media expert helped her with her Twitter account, although she could not explain what exactly the social media expert had done.

Power relations in networks operate as forces in tweet and hashtag assemblages and influence their rhizomatic movement. In her interview Maria criticised the use of retweets as endorsements to promote content or to promote other Twitter users, who in turn may bring followers to the person who retweeted. Conversely, relational cross- reading of data showed that research participants acted differently towards network actors, who were perceived to be ‘experts’ or who were central to a particular network. An example is provided by Marc, who tweeted: “@user And you should definitely follow [name]. Apart from being a nice hombre he's also very knowledgeable yet

humble.” (Marc, tweet). Rachel stated in her interview that she had read and thought about ‘filter bubbles’, i.e. “that we follow the same kind of people” (Rachel, interview) and that this Twitter practice may lead to closed networks. She then reported about a newspaper article which suggested “swapping political opinions” (Rachel, interview) with social media user who have a different opinion from one’s own, in order to work against the emergence of such ‘filter bubbles’.