Elon Musk joined twitter in 2009, however only his tweets from the year 2016 are available. In a defamation case, a Jury declared Musk not liable for a tweet in which he called a British rescue diver ‘pedo-guy’. In arguing his case Musk’s attorneys exclaimed that Twitter is “infamous for invective and hyperbole” where users “play fast and loose with facts”, and that forums like Twitter “are not a source of facts or data upon which a reasonable person would rely”. Twitter participants “expect to read opinions not facts” (Mitchell, 2019). These are strong words from those that surround Elon Musk, but they could inform and allow some insight into how Elon Musk uses the platform of Twitter.
With over 40 million followers (Socialbakers) Musk is a large presence on social media, known often for his unconventional tweets for someone with such a large following.
The target audience his twitter reaches can often be seen directly through the tweets he produces. Action film loving technology fanatics could be one definition of those who follow Musk on twitter, tuning in for his edgy technology tweets along with informative tweets on how his companies are running.
Collecting Research and data on Elon Musk
To identify how Elon Musk utilises social media to communicate online, and how this can be related to relevant media theories, I will need to collect research and data. To begin with I need to identify a solid research question. I could look at more methodological questions, with analysis and visualisation, or potentially questions of scale and discuss big data and data collection. (McCay-Peet and Quan-Haase, 2017). In the blog I will apply two theories to analyse the behaviour on social media of Elon Musk and how he uses social media to engage with his audience, therefore a good research question and data collection needs to inform these upcoming blog posts. The two theories I have decided to focus on are Habermas’ theory on the public sphere and Goffman’s performance of self theory.
One route I can take to understand communication I will look at sentiment analysis from both a qualitative and quantitative viewpoint. This data will inform how and why Elon Musk engages with his audience. I can begin by looking at Elon Musk’s tweet frequency, to gain an understanding on the habits he undertakes as a social media user. Twitter sentiment analysis provides the methods to identify public emotion about events and products that relate to them. Sentiment can be obtained through features by analysing lexical and syntactic features, these features are expressed through sentiment words, emoticons and exclamation marks (Jianqiang et al, 2018). Word-Emotion Association also known as NRC Emotion Lexicon, lists words with eight emotions; anger, fear, anticipation, trust, surprise, sadness, joy and disgust, as well as two sentiments of negative and positive (Mohammad, n.d.). I can apply this to Elon Musk’s tweets by collecting the posts he has made and categorising the words using NRC lexicon. It will further allow me to understand how he communicates with his fan base, and I can use the data in correlation with Goffman’s theory. I will look at how his sentiment changes over time in relation to events that have taken place in his life.
Word clouds are a straightforward and visually appealing method to provide an overview of text by distilling it down to words that appear with the highest frequency (Heimerl, Lohmann, Lange and Ertl, 2014). A world cloud can be used to further emphasise sentiment research and can be effective to better understand the sentiment used by Elon Musk. I will have to apply a word cloud to Musk’s twitter, however some common words which are little value in helping the need of the word cloud have to be excluded. These are called stop words, and the general strategy for determining a stop list is to sort the terms by collection frequency (Stanford, n.d.).
Netnography is a qualitative form of research that explores consumer behaviour and digital tribes by ethnographic research that is conducted online (Bartl, Stockinger and Kannan, 2016). Kozinets’ studies of 1990’s fan culture (1998) informed him of the extent in which fan cultures embraced online discussions. This highlighted the lack of well constructed methods of research that can deal with large volumes of data being generated as well as the ethical issues that can be associated with researching online communities. (Costello, McDermott and Wallace, 2017)