Change-Point Analysis of the Public Mood in UK Twitter during the Brexit Referendum

Lansdall-Welfare, Thomas; Dzogang, Fabon; Cristianini, Nello

doi:10.1109/icdmw.2016.0068

Cited by 31 publications

(25 citation statements)

References 11 publications

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“…This, in conjunction with the lack of practical tools, has made the analysis of social media data difficult and challenging for researchers (Archak et al, 2011;Netzer et al, 2012;Stieglitz et al, 2018). In addition, several studies also note that traditional analytical techniques are not suited to the analysis of vast amounts of data on a daily basis (He et al, 2017;Lansdall-Welfare et al, 2016). This research differs from other studies in that it uses Twitter as its data source and integrates different social media analytics techniques to offer possible solutions to these challenges and enhance the literature.…”

Section: Efficiency Of the Approachmentioning

confidence: 92%

“…Event detection can be defined as a process of detecting novel events from text corpus . Several studies have demonstrated effectively event detection analysis in variety of settings including political events (Lansdall-Welfare et al, 2016), disasters predictions (Naaman, Becker, & Gravano, 2011), epidemic disease outbreaks (Odlum & Yoon, 2015), or market predictions (Daniel et al, 2017). Often, high frequency phenomena, which exhibit uncommon trending and draw people attention, are regarded as events (Kaneko & Yanai, 2016).…”

Section: Event Detection On Social Mediamentioning

confidence: 99%

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Decoding the sentiment dynamics of online retailing customers: Time series analysis of social media

Ibrahim

Wang

2019

Computers in Human Behavior

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Decoding the sentiment dynamics of online retailing customers: Time series analysis of social media. Computers in Human Behavior, 96,[32][33][34][35][36][37][38][39][40][41][42][43][44][45]. https://doi. AbstractThe Twittersphere often offers valuable information about current events. However, despite the enormous quantity of tweets regarding online retailing, we know little about customers' perceptions regarding the products and services offered by online retail brands. Therefore, this study focuses on analysing brand-related tweets associated with five leading UK online retailers during the most important sales period of the year, covering Black Friday, Christmas and the New Year's sales events.We explore trends in customer tweets by utilising a combination of data analytics approaches including time series analysis, sentiment analysis and topic modelling to analyse the trends of tweet volume and sentiment and to understand the reasons underlying changes in sentiment. Through the sentiment and time series analyses, we identify several critical time points that lead to significant deviations in sentiment trends. We then use a topic modelling approach to examine the tweets in the period leading up to and following these critical moments to understand what exactly drives these changes in sentiment. The study provides a deeper understanding of online retailing customer behaviour and derives significant managerial insights that are useful for improving online retailing service provision.Ibrahim, Wang, and Bourne 2017). In addition, an overwhelmingly large volume of data (Netzer, Feldman, Goldenberg, and Fresko 2012) and a lack of practical tools with which to analyse unstructured data (Archak, Ghose, and Ipeirotis 2011) also complicate such analysis. Since Twitter content is produced abundantly every day, changes in customer sentiment and the relationship between those changes and emerging issues on Twitter are difficult to capture. The root cause of such changes may lie in a range of factors, including external events, accidents and disasters, and users' daily lives.Therefore, to understand what exactly drives changes in customer sentiment, this study attempts to exploit the content of these real-time data to understand trends in customer tweets. Thus, the research questions are as follows:• What are the volume and sentiment trends of online retailing brand-related tweets on Twitter?• What are the main reasons behind the changes in the volume and sentiment trends of online retail brand-related tweets?• How can online retailers learn from social media users to improve their online retailing service provision?To answer these questions, we analyse changes in customer sentiment and the underlying customer concerns that evolve through the time series. We examine those tweets or comments posted by customers that are directly related to five leading UK retailers namely Amazon, Argos, Asda, John Lewis and Tesco during the period spanning Black Friday, Christmas, Boxing Day and New Year's Day in the United Kingdom. Using a ...

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Section: Efficiency Of the Approachmentioning

confidence: 92%

Section: Event Detection On Social Mediamentioning

confidence: 99%

Decoding the sentiment dynamics of online retailing customers: Time series analysis of social media

Ibrahim

Wang

2019

Computers in Human Behavior

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“…In the 4 years intervals the social platform not only reacted to the seasons but it also responded to other significant events: for example in a recent study we found common change-points between the moods in Twitter and the gbp/euro rate in the hours following the vote for the EU membership referendum (brexit) [5].…”

Section: Resultsmentioning

confidence: 99%

Seasonal Variation in Collective Mood via Twitter Content and Medical Purchases

Dzogang

Goulding

Lightman

et al. 2017

Advances in Intelligent Data Analysis XVI

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Abstract. The analysis of sentiment contained in vast amounts of Twitter messages has reliably shown seasonal patterns of variation in multiple studies, a finding that can have great importance in the understanding of seasonal affective disorders, particularly if related with known seasonal variations in certain hormones. An important question, however, is that of directly linking the signals coming from Twitter with other sources of evidence about average mood changes. Specifically we compare Twitter signals relative to anxiety, sadness, anger, and fatigue with purchase of items related to anxiety, stress and fatigue at a major UK Health and Beauty retailer. Results show that all of these signals are highly correlated and strongly seasonal, being under-expressed in the summer and over-expressed in the other seasons, with interesting differences and similarities across them. Anxiety signals, extracted from both Twitter and from Health product purchases, peak in spring and autumn, and correlate also with the purchase of stress remedies, while Twitter sadness has a peak in the Winter, along with Twitter anger and remedies for fatigue. Surprisingly, purchase of remedies for fatigue do not match the Twitter fatigue, suggesting that perhaps the names we give to these indicators are only approximate indications of what they actually measure. This study contributes both to the clarification of the mood signals contained in social media, and more generally to our understanding of seasonal cycles in collective mood.

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“…A body of work [12,13] has begun to explore Brexit opinion and sentiment as expressed on Twitter. Howard and Kollanyi [9] share our interest in propaganda, but their work concerns "bots" and the role of automated activity on Twitter.…”

Section: Related Workmentioning

confidence: 99%

Quantifying Media Influence and Partisan Attention on Twitter During the UK EU Referendum

Gorrell

Roberts

Greenwood

et al. 2018

Lecture Notes in Computer Science

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User generated media, and their influence on the information individuals are exposed to, have the potential to affect political outcomes. This is increasingly a focus for attention and concern. The British EU membership referendum provided an opportunity for researchers to explore the nature and impact of the new infosphere in a politically charged situation. This work contributes by reviewing websites that were linked in a Brexit Tweet dataset of 13.2 million tweets, by 1.8 million distinct users, collected in the run-up to the referendum. In this dataset, 480,000 users have been classified according to their "Brexit" vote intent. Findings include that linked material on Twitter was mostly posted by those in favour of leaving the EU. Mainstream news media had the greatest impact in terms of number of links tweeted, with alternative media and campaign sites appearing to a much lesser extent. Of the 15 most linked mainstream media, half show a substantially greater appeal to the leave camp, with two of them very much so. No mainstream media had a consistent appeal among remain supporters. Among the sites that were highly favoured by one voter valence or the other, the leave sites had by far the greatest impact in terms of number of appearances in tweets. Remain-preferred sites were less linked, and dominated by explicit campaign sites. Leave-preferred sites were more numerously linked, and dominated by mainstream and alternative media.

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Change-Point Analysis of the Public Mood in UK Twitter during the Brexit Referendum

Cited by 31 publications

References 11 publications

Decoding the sentiment dynamics of online retailing customers: Time series analysis of social media

Decoding the sentiment dynamics of online retailing customers: Time series analysis of social media

Seasonal Variation in Collective Mood via Twitter Content and Medical Purchases

Quantifying Media Influence and Partisan Attention on Twitter During the UK EU Referendum

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