Hashtags

Abstract

The presence of certain textual elements specific to social media is ubiquitous and has transcended social media. Hashtags and emojis are now present in a number of discourse types and are even used in spoken language. While emojis carry out the function of expressing sentiment or emotions, as we saw in the previous chapter, hashtags attempt to condense a complex idea into a textual sequence of varying length with the aim of sharing and quickly disseminating it. This chapter contains a description of the most relevant hashtags used in the CCTC, focusing on the differences found among several countries, which reveal significant differences between them.

According to Zappavigna (2011, 789), hashtags function as linguistic markers enacting the following social relation “search for me and affiliate with my value!” This is certainly the original function of hashtags, but their prevalence in social media is such that this original function has now been extended to fulfil more complex roles in the communication of the speakers’ message itself, not just on Twitter/X or other social media platforms, but in offline written contexts and even face-to-face communication (Scott 2018).

Thus, hashtags have progressively become units of meaning that permit great creativity, as they function similarly to memes. From this perspective, hashtags successfully encapsulate an idea, socio-political view or vindication, which is then ready for fast and far-reaching dissemination on the Internet and beyond. This is exactly what internet memes pursue, as defined by Dawkins (1976), i.e. “a unit of cultural transmission”.

Hashtags are commonly used in sociological studies to track online perception of current affairs, as their frequency and context can be used as a proxy to measure the stance that users have towards certain political or social events or ideologies. For example, Anderson (2016) tracked the use of the hashtags #BlackLivesMatter, #AllLivesMatters, and #BlueLivesMatters. The first one, which predated the Black Lives Matters organization, was used approximately 12 million times from July 2013 until March 2016, where the vast majority of the tweets were in solidarity with the movement, with only a small proportion (11%) used to criticise it. However, after the shootings of police officers in Dallas and Baton Rouge in July 2016, the three hashtags displayed increased frequency, accompanied by a change of tone changed around the #BlackLivesMatter hashtag, as well as a dramatic rise in the share of tweets criticising the Black Lives Matter movement.

A piece of research that shows how hashtags have transcended the social networking realm is the article by Dobrin (2020), who uses qualitative content analysis through the lens of cultural studies on a corpus of 200 articles where the hashtag #MeToo was included. The hashtag itself is found to be “a cultural object that perpetuates the movement’s political agenda in the public sphere and bridges personal and collective experiences under the #MeToo myth” (p. 1). Obviously, the astounding success of the #MeToo hashtag on society has crossed borders and languages, and has made an exceptionally strong impact on general media and, ultimately, on society.

Research on the use of hashtags during the COVID-19 pandemic is also abundant, most of the the studies combine topic modelling and hashtag analysis, although some focus specifically on the latter, such as Cruickshank and Carley (2020)

7.1 Hashtags in the CCTC

The brief study that follows, which employs the same corpus as the preceding chapter, aims to provide a general overview of the most popular hashtags used during the pandemic in the top six countries by volume, highlighting their similarities and differences and how they reflect the societies that generated them. Hashtags are very easy to extract from text, as it only involves a simple regular expression, such as ‘#\w+’. The script I use extracts hashtags from each country subcorpus and generates counts and relative frequencies per 1,000 words, aggregated by week, so that the frequency of individual tags can be compared across countries and tracked over time. Table 7.1 shows the top 50 hashtags of each of the six countries in the corpus for the whole period.¹

Table 7.1 Top 50 hashtags by country

This table includes most of the hashtags common to all countries, which, if we account for variations of the same word (i.e. #COVID-19, #Covid_19, #Covid, etc.), is fairly limited: #COVID-19, #Coronavirus, #WearAMask, #vaccine, #lockdown, #StayHome, and #staysafe. In addition, a few others were present in all lists except #COVIDIOTS, only missing in India, where it ranks 80th, and South Africa, where it ranks very low (in 425th position), and #pandemic, only missing in South Africa, where it ranks 55th. Therefore, the most frequent type of hashtags across all countries were those of an exhortative nature, encouraging others to follow safety precautions.

The #COVIDIOTS hashtag goes a step beyond and aims to punish those that do not abide by these recommendations or laws, as examples (59) to (61) show.

• 59. If you think that please stop shopping in stores, just order things online. #COVIDIOTS Everything went down hill when Cats came out.

• 60. CDC: 38% of the attendees at an Arkansas church over a week contracted coronavirus #COVIDIOT #TrumpVirus #COVIDIOTS.

• 61. Let's hold back on what we WANT until a #COVID19Vaccine is available/working. #COVIDIOTS Great news for our community.

All other hashtags are specific to each country. Table 7.2 lists them after removing those that simply refer to the country itself (e.g. #southafrica, #UK, #COVID19Aus).

Table 7.2 Country-specific hashtags

Country-specific tags do provide a good picture of the particular social and political contexts. Some are irrelevant news aggregators, as in the case of Canada and Australia (e.g. #cdnpli, #bcpoli, #AusPol, #nswpol), but in general the differences are useful to study the idiosyncrasies of different countries and societies. In the United States and South Africa lists there is a significant presence of politics-related words. Both share racism-related tags: #BlackLivesMatter and #BLM in the former, #RacismMustFall, and #RacialProfiling in the latter. They also share elections-related tags (#BidenHarris2020, #VoetsekANC), as both countries had general elections during the period or recently before it (2020 in the United States, 2019 in South Africa).

The high position of the #BlackLivesMatter and #BLM tags during the two years covered by the corpus was no doubt due to the public outrage and subsequent protests caused by the death of George Floyd on June 6, 2020, which is clearly reflected in Fig. 7.1.

Fig. 7.1

Frequency of #BlackLivesMatter and #BLM (per 1,000 words)

The political polarization of the United States is reflected more directly by the high-ranking #TrumpVirus and #MAGA tags, but also by the fact that there are several in reference to the use of facemasks (#MaskUp, #WearADamnMask), and vaccines (#GetVaccinated, #GetVaccinatedNow, #VaccinesWork), two aspects of the pandemic that became increasingly politicised. The fact that the only two geographical locations mentioned in the set of hashtags are Florida and Texas is also telling of the politicisation of the pandemic, as the Republican majority of these two States led to more permissive policies concerning stay-at-home orders and mask mandates. Similarly, although not present in the top 50 list, there is a plethora of tags that criticize the management of the pandemic by Trump’s Administration: #TrumpLiesAmericansDie (position 95), #TrumpIsANationalDisgrace (110), #GOPBetrayedAmerica (121), #TrumpKnew (144), #TraitorTrump (196), #GOPDeathCult (213), #TrumpIsALaughingStock (214), #TrumpFailedAmerica (234), etc.

Other countries also display some politics-related tags: #Brexit in the United Kingdom, #VoteFordOut2022 in Canada, and #IStandWithDan in Australia. The Australian tag was deliberately created and made viral to support the Victorian State Government’s handling of the pandemic, in reference to its Premier, Daniel Andrews, during the second wave in the second half of 2020. This hashtag was in opposition to the condemning #DictatorDan, which is in position 121 in terms of frequency in the CCTC.

Graham et al. (2021) conducted a comprehensive, mixed-methods study of this phenomenon, which showed how a small number of hyper-partisan pro- and anti-government campaigners were able to create ad hoc communities on Twitter that generated a considerable amount of political mobilisation. Their Twitter dataset contained data from March to September 2020 (nearly 400,000 tweets). Their quantitative data closely match ours for that period: a few weeks after #DictatorDan first appeared, #IStandWithDan quickly overwhelmed it, and then both tags fought for dominance over time, with a clear prevalence of the latter. Figure 7.2 plots the relative frequency of these two hashtags over the two years that our data cover.

Fig. 7.2

Frequency of #DictatorDan vs. #IStandWithDan (per 1,000 words)

It is surprising how this polarization was maintained long after the phenomenon started: although it subsided briefly at the end of 2020, it gathered considerable momentum at several points during 2021, which only goes to show how difficult it is to put out the flames of polarization once they have been ignited. It is also a good example of how social networks are used by political campaigners to gain support and votes, at the expense of social confrontation.

The mental health topic is only present in U.K.’s hashtags (#mentalhealth, #SuicideAwareness), although it is present in all countries with different relevance (as measured by their frequency): #mentalhealth is in 54th position in Canada, 69th in Australia, and 106th in the U.S. Again, we find significant differences between this group of countries, on the one hand, and South Africa and India, on the other; in the former, it is in position 220 and 438 in India. Figure 7.3 shows a visualization of these data based on relative frequency of the hashtag #mentalhealth in these countries.

Fig. 7.3

Heatmap of #mentalhealth by country