Posted by: UM London | Date added: Mon 10 Sep 2018
How do we really feel about the infamous British weather? A new analysis of how we used emojis in social media during the recent heatwave busts the myths and also shines a light on key differences in how men and women convey emotions on social channels.
Richard Page, Data, Insights and Technology Manager at IPG Mediabrands, explains: “The stereotypical view of Brits is often aligned to our usually gloomy climate and we commonly hear that we’re taciturn and repressed as a nation. However, the research we conducted during the heatwave shows nothing could be further from the truth.
“Perhaps contrary to expectations, a much higher proportion of us greatly enjoyed the hot spell than did not – and in fact were positively joyous about it. We were also highly vocal about broadcasting our emotional state – whether that’s positive or negative.”
Eight of the ten emojis used most often during the heatwave were emotional and five of that number were unambiguously positive. The happiest town during the heatwave (of those with more than 100 emotional posts) was Robin Hood’s Bay in Yorkshire with 89% Joyous mentions, followed by St Albans (82%), Westminster (77%), Harrogate (75%) and Lancaster (74%).
Meanwhile, the grumpiest places were mostly in London and the South East – Faversham in Kent recorded the highest temperature of 35.3oC on 26th July. Watford topped the charts with 44% negative mentions overall, followed by Islington (42%), Chelmsford (40%), Croydon (40%) and Aberdeen (40%).
Tim Sanders, Data Science Director at IPG Mediabrands UK, adds: “The study has proved very useful in confirming or challenging long-held assumptions. It’s not all that surprising that the old ‘stiff upper lip’ stereotype no longer holds water, nor that people are less happy in the hottest places - and typically urban areas that generate their own micro-climate.
“However, what is useful, particularly to the brands we work with, is additional insight into the gendered use of social media. This confirms emotion is a key factor to consider when marketing to males and females on social channels, and also suggests what particular emojis will resonate the most at any given time.”
Women display more emotion than men – at least on social channels. While males were (slightly) more vocal about the heatwave, accounting for 51% of mentions, their tweets displayed less emotion - 43% of male tweets were neutral, as compared to 36% of female posts. However, what is notable is that women used 66% more emojis than males.
Sanders continues: “It seems women are more comfortable in displaying high levels of emotion through their emojis, for example ‘Smiling Face with Heart Eyes’ and ‘Loudly Crying Face’, whereas males tend to use more reserved representations.”
The analysis was carried out by media agency IPG Mediabrands using HEART, a software tool that integrates with IBM’s Watson AI to add an additional layer of analysis to increase accuracy in reviewing sentiment on open social channels. The Watson AI is able to identify emotion in language, while HEART reads emoji visual cues on a more granular level than has been possible previously. The project analysed 135,710 social media posts from 82,902 Brits from the official start of the heatwave 23rd June until 14th August.
Other key findings on British attitudes to the extreme weather identified by the study include:
• Overall, Brits were twice as likely to be happy about the higher than average summer temperatures. A third (33%) of UK heatwave mentions were Joyous, while ‘Sadness’ (10%), ‘Anger’ (3%), ‘Fear’ (2%) and ‘Disgust’ (1%) accounted for 17% of mentions.
• People were happiest at 28oC, with ‘Joy’ accounting for 72% of emotional mentions at that temperature. 34oC is the upper tolerance level for most of us - 41% of emojis used at that temperature were negative.
• We found things particularly difficult when daily temperatures rose by three or more degrees. On days when this happened, ‘Joy’ mentions increased by an average of 59%, but ‘Sad’ mentions increased by 77% on average.
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