#Covid-19

The past year has affected all of us in one way or another but have you ever thought about what effect it may have had on our language?  Philipp Wicke and Mariana Bolognesi did just that in their study of thousands of tweets posted during March and April 2020.

Due to social distancing measures, people were quick to use social media platforms like Twitter to connect with others and express their feelings, sending around 16,000 tweets an hour with hashtags like #coronavirus, #Covid-19 and #Covid. The researchers wanted to explore this online discourse and were particularly interested in how the pandemic was discussed using the metaphor of war. Discourse about disease has often been found to use this metaphor and cancer patients frequently complain that they are described as being in a 'battle' with the illness, which they find negative and unhelpful. With this in mind, Wicke and Bolognesi decided to also explore other figurative ways in which Covid was being described.

They collected 25,000 tweets a day that contained at least one of eight covid-related hashtags. Retweets were not included nor were more than one tweet per user in order to gain a balanced view of language use. 5.32% of the collected tweets mentioned war, the most common words being 'fight' (29.76% of these mentions) and 'war' (10.08%), whilst 'combat', 'threat' and 'battle' were also prevalent. The researchers noted that this could reflect this early stage of the pandemic: it was a global emergency and urgent action was needed to confront the situation. Most of these examples referred specifically to the treatment of the virus and the 'frontline' workers dealing with its effects in hospital.

When they concentrated on other figurative ways in which Covid was being described they found it referred to in terms of a storm, a monster and a tsunami.  For example, the idea of the virus as a storm arose in 1.49% of the tweets and contained words like 'thunderstorm', 'rain' and 'lightning'; 1.13% of the tweets referred to a tsunami, using words like 'earthquake', 'disaster' and 'tide' and references to a monster occurred in 0.68% of the tweets with 'freak', 'demon' and 'devil' being prime examples. These negative images mainly referred to the onset and spread of the virus. It is clear, however, that the war metaphor was used significantly more than these others.

Wicke and Bolognesi conclude that their results confirm previous findings that the war metaphor is common in public discourse of disease; however, they found that it was used very particularly during the first weeks of the pandemic to refer to the initial medical response to it. They also suggest that all of these metaphors are negative and unhelpful and propose the construction of a 'Metaphor Menu', previously suggested with regards to cancer, to give the public more positive and desirable ways to talk about Covid 19 as the pandemic evolves and changes.

------------------------------------

Wicke P., M. M. Bolognesi (2020) Framing COVID-19: How we conceptualize and discuss the pandemic on Twitter. PLoS ONE 15(9): e0240010. 

https://doi.org/10.1371/journal.pone.0240010

This summary was written by Gemma Stoyle

Why we use emoji: Written gestures in online writing

When we talk to each other, we don’t just rely on words. Emotion is embodied, and our expressions, our body language, our tone of voice are all used to convey our feelings and affect how our words are interpreted. But for online written communication, we can’t rely on these details. As discussed in the previous post, punctuation can be helpful to represent tone of voice, but often there is still something missing. In the fifth chapter of her pop linguistics book Because Internet, Gretchen McCulloch explores how emoji became popular as a way of replicating gestures in online communication.

Emoji cannot be considered a language: there is a limit to what can be expressed, and most languages can handle meta-level vocabulary about language, which emoji cannot. But they clearly do something. However, many popular emoji use hand and facial gestures, which, McCulloch says, inspired her to begin treating them as gesture.

There are two types of gesture which emoji can represent: the first are called emblems. These are nameable gestures, and have precise forms and stable meanings, and are often culturally specific, such as winking, giving a thumbs up, and obscene hand gestures. Many of these have directly equivalent emoji, for example, fingers crossed 🤞, rolling eyes 🙄, or a peace sign ✌️. Some emoji are more metaphorical, such as the eggplant emoji as a phallic symbol, but, with knowledge of internet norms, they still have fixed meanings. Emoji are not the only way to express emblems online: reaction gifs and images are also used to express specific moods or actions, many of which we can refer to by name (for example, most internet-literate people will know what I mean by Michael Jackson Eating Popcorn.gif).

The second type of gesture with corresponding emoji are illustrative or co-speech gestures. These gestures are dependent on surrounding speech, and highlight or reinforce the topic. You often make these without realising, and at times when they make little sense, such as waving your hands around when on the phone and your conversational partner can’t see you. These gestures don’t have specific names but can be described. Think of the way you move you your hands when giving somebody directions or describing the size of something. These gestures are also represented in emoji. The example McCulloch uses is the range of emojis possible in a ‘Happy Birthday’ message, perhaps a combination of the following 🎂🍰🎁🎊🎉🎈🥳. In these contexts, the order doesn’t matter, these emoji aren’t telling a story, they are adding to the current one. Illustrative emoji are also more likely to be taken at face value, and don’t necessarily require knowledge of internet culture that, for example the eggplant emoji might require. If emblems are for the benefit of the listener, then illustrative gesture are for the benefit of the speaker, used to help them get their message across.

McCulloch also examines common sequences of emoji, finding that, unlike words, emoji are often repeated, both as a straightforward sequence of the same emoji multiple times (the most common being 😂), and sequences of different emoji that are linked thematically, such as the series of birthday related emoji above, or a series of love emoji such as 💕💓😍💗🥰💖. This is another reason why emoji can be considered gesture: repetition does not generally occur in our words, but does occur in hand gestures.

Repetitive gestures are known as beat gestures: they are rhythmic, and if you stutter while you speak, your gestures also do the same. Emoji also do this: we type 👍👍👍 to represent a sustained or repeated thumbs up gesture in real life. We can even repeat emoji which don’t have a literal gesture attached, because, as a whole, emoji can be repeated. The ‘clap back’ is a common beat gesture among African American women, and this is often represented through emoji as a form of emphasis: 👏 WHAT 👏 ARE 👏 YOU 👏 DOING 👏

Emoji serve an important purpose in informal written communication, filling in for expression and gesture which otherwise are hard to convey. For more from McCulloch on the topic of emoji and gesture, Episode 34 of her podcast Lingthusiasm with Lauren Gawne, discusses the content in this chapter, and provides several further links on the topic of emoji and gesture.

------------------------------------------------------------

McCulloch, Gretchen. 2019. Because Internet: Understanding the New Rules of Language. New York: Riverhead Books.

This summary was written by Rhona Graham

You are what you Tweet!

In the time that it takes you to read this article, millions of users will have sent a Snapchat, uploaded an Insta Story and updated their Twitter profile. The age of digital culture is very much upon us. For Linguists, the contemporary networked society offers a way to explore language use beyond the traditional method of recording and interviewing speakers. This includes those studies which examine the dialectal distribution of words and features across different parts of the country. One such paper is Grieve and colleagues’ recent Twitter-based analysis of lexical variation in British English.

Traditionally, linguists interested in researching dialectal variation (i.e., linguistic features specific to a particular geographic region or group) have set about researching this topic by conducting surveys and interviews with speakers of a particular variety. For instance, a linguist might ask someone to name the “a narrow passageway between or behind buildings”. If you’re from the south, you might say ‘alleyway’ but northern speakers might call it a ‘snicket’ or a ‘ginnel’.

With the advent of social media, however, linguists no longer have to elicit these words directly. Rather, they can extract massive datasets of social media data to examine where in the country these words are used most.

In their 2019 paper, Grieve and colleagues used a corpus (i.e., dataset) of 180 million Tweets to examine lexical variation in British English. Helpfully, since tweets include what is known as ‘metadata’ that relates to the location in which the tweet was sent, Grieve and colleagues were able to plot these tweets on maps to identify where these words were most frequent. They compared their analysis with the more traditional approach taken in the BBC Voices project.

Their analysis very convincingly shows that the lexical variation observed in the Twitter data mirrors that identified in more traditional analyses! This finding is shown in the graphic below, where for all of the 8 words, the Twitter maps look comparable to those created for the BBC Voices project. For instance, consider the maps for the word ‘bairn’ – a word that means ‘child’ is typically heard in northern UK dialects (second row, right). The BBC Voices project map and the Twitter map are virtually indistinguishable. Across both maps, this word appears largely confined to the north/north-east of the UK – as expected.

Whilst, for the most part, the traditional dialect maps and the Twitter dialect maps look very similar, Grieve and colleagues note some differences. For instance, in the Twitter dataset, ‘bairn’ is observed to account for a maximum of 7.2% instances of the word ‘child’, even in the areas where it is stereotypically associated with that dialect. This is in comparison to the BBC Voices dataset, which reports a maximum of 100% of instances of ‘bairn’ for ‘child’ in some areas. Discussing the reasons for this difference, Grieve and colleagues explore several possibilities. First, they suggest that the differences may be related to a decline in usage of this word. It is possible that 'bairn' has simply become less popular over time. However, the decline in the use of this word also might have something to do with the type of data we get from Twitter and the way it's analysed in large-scale studies such as this. In particular, the authors note that it is impossible to examine the conversational context of the tweet. A such, it’s possible that’s there’s some contexts where users would use ‘child’ for ‘bairn’ even if they use the dialectal term ‘bairn’ in speech. For instance, if a user is reporting someone else’s speech.

Nevertheless, with these issues aside, Grieve and colleagues’ analysis suggests that the findings observed in large-scale dialectal surveys are largely mirrored in the Twitter data. As such, we can expect more and more sociolinguistic research to examine data from social media sites, such as Twitter in the future! So, it seems, you really are what you tweet!

------------------------------------------------------------

Grieve, Jack; Chris Montgomery; Andrea Nini; Akira Murakami & Diansheng Guo (2019) Mapping Lexical Dialect Variation in British English Using Twitter. Frontiers in Artificial Intelligence. 

This summary was written by Christian Ilbury

https://doi.org/10.3389/frai.2019.00011.