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#GeneralElectionNow: A Cry For Help With Twitter Trends


The current Conservative regime under the leadership of PM Rishi Sunak has been proven inadequate in meeting the crisis in the UK. As the UK fights recession, stagflation, and migrant crisis, the government’s flaws creep slowly into the light. In November 2022, thousands gathered in central London to demonstrate against what they called “Tory austerity.” Hundreds raised the call for general elections in the protest, otherwise known as “Britain is broken.” People marched in the rain from Embankment to Trafalgar Square in Central London, protesting against the Tory government. Some of the main issues among the people included funding of the NHS. Desperate for change, the citizens of the UK have, time and again, taken to social media showcasing discontent with the government.

Twitter has been abuzz with calls for action to save the flailing democracy that is the UK. There have been multiple protests in the UK by workers for improved pay and rights. The people seem determined to get the government to heed their needs. Sudden leadership changes and failure to fulfill their election manifesto have sparked mass restlessness. People want change in their way of life. As a result, the cost of living, rough sleepers, and poverty have increased. However, the government still needs to successfully mitigate these issues that have given rise to this mass movement. This study analyses the Twitter hashtag- #GeneralElectionNow, bringing to light the government’s failure and the people’s needs.


This report details a broad analysis of tweets containing the term ‘GENERAL ELECTION NOW’ gathered between January 11, 2023, and January 14, 2023. The resulting tweets span a time frame of 7:30 pm-11:30 am, spanning 64 hours. This report makes an in-depth analysis of the data set acquired. The total number of tweets analyzed was 16,509, of which 4,198 were e-tweets, and 12,311 were retweets. Depicted below is the pie chart representing the number in percentage.

Of the total tweets, 74.5% were retweets, and 25.5% were original tweets. However, Boyd et al. (2010) argued that retweets were not only conversational but turned the linear conversation into broader, overlapping discourse, often with additional meaning. Majmundar et al. (2018) simplify the explanation with their “Why We Retweet scale,” which suggests that retweets are equally valid forms of expression as original tweets, able to reflect a range of positions, including- approval, argument, attention, and entertainment. For these reasons, retweets are included across all aspects of the analysis unless otherwise specified. Moreover, it is essential to understand the ratio of the total number of tweets to the total number of users.

In this study, the total number of tweets is 16509, and the total number of users is 5954, which means that one person almost tweeted 2.77 times. This ratio helps in understanding the significance of the trend and the level of engagement by the users. Below are the top 5 most retweeted tweets. The highest number of tweets was retweeted 259 times and was made by @dave43law. Surprisingly the tweet was not caused by any user among the top 10. However, Dave Lawrence (@dave43law), with a botomoter score of 2.7, is attributed to have topped the charts, with 6 of his tweets being in the top 6 retweeted. As per the botomoter, he is a natural person. User Dave Lawrence can be attributed to having had the most original tweets, with a total of 1005 retweets on his top 5 tweets.

Of all the users tweeting during 64 hours, the majority of users tweeted just once. However, the most prolific user tweeted 341 times. This imbalance of participation is standard on the internet, forming a power law distribution. Clay Shirky’s Here Comes Everybody documents this phenomenon, saying, “social participation in many other large-scale systems all exhibit a similar pattern.” 

Looking at the most retweeted tweets containing the search terms, all of the top five are critical of the current regime and want immediate change in leadership. Such actions depict that the public reaction on Twitter was largely adverse. A horizontal bar graph below depicts the top 10 users and their total number of tweets.

Chirpy Chet (341), Frances Carroll (256), raycasey2003 (243), simunch1 (222), and LesleyM25724591 (208) formed the Top 5 users. Again, the top tweeters contributed significantly more than average, with the ultimate user releasing 341 total tweets, over 10x of the average. The range of values within the top 10 tweeters was still surprisingly divided, with the top three tweeters producing a combined 840 tweets compared with 1222 total tweets by the remaining seven. All in all, the top ten tweeters contributed 12.49% of the entire sample.


People took to Twitter, mainly across the UK, as with any popular hashtag, to discuss the situation. Using the hashtag #GeneralElectionNow combined with the word “TORIES,” the conversation ensued. Before analyzing the data, as many bots as possible were removed to maximize the validity of the data. A total of 6 bots were identified and removed from the most prolific tweeters, and many more were identified by looking at the usernames and finding self-identified bots. To help understand the volume of tweets per day, a line graph below showcases the total number of tweets on a particular date. The maximum number of tweets was recorded on January 12, 2023, with 6949 tweets in 24 hours. This tweak has been noticed mainly after midnight.

While the number of tweets started relatively low, they increased steadily. While there may appear to be a few dips in tweet rate, Twitter seemed abuzz after the Prime Minister’s Questions were held on January 11. Particular spikes came after midnight. During this week’s PMQs, PM Rishi Sunak was vehemently quizzed about the deplorable condition of the NHS. This section delves more deeply into the volume of tweets per day and hour, and lastly, focuses on the hour when most tweets were recorded. The proceeding sections emphasize the significance of the social media application as a platform for the public to voice their thoughts. Depicted below is a scatter chart analyzing tweets per 8 hours. Notably, there was a spike after the 24th hour.

Tweets including these two terms increased up to and throughout the analysis, reaching a peak shortly after the PMQs ended. Given the enduring nature of the cost of living crisis, the rising cold, and rampant strikes across the UK, the conversation swelled over the period, but this certainly wasn’t the start or end of the discussion. Creating an hourly graph for 64 hours was a cumbersome task. Therefore, the span with the most traffic was carefully examined. Apart from analyzing the tweets daily and hourly, I have studied the tweets recorded on January 12 between 1:00 am and 2:00 pm. During the said period, a maximum number of tweets were recorded. The highest was up to 150 tweets per minute.

The above diagram showcases how people took to Twitter to criticize the Government. The timeline displayed here reflects the volume of tweets made each minute. Expectedly, the sharp upward trajectory reflects the moments immediately. The average tweet rate was 56 tweets per minute (TPM) between the given days. A notable discourse is likely a reflection of an anticipatory news cycle. More users had the free time to engage in online discourse after the PMQs. Curiously, a slight uptick in tweet volume reaching 150 tweets, appears to proceed a much more significant increase in overall volume, perhaps suggesting a specific community was informed of the deal’s confirmation before it went into mainstream conversation. After midnight on January 12, the count of tweets per minute more than tripled to the prior average, reaching a peak within half an hour. However, the rise is followed by a steadier decline, with an average of 56 tweets per minute.


Utilizing the online ‘Botometer’ tool, all top 10 accounts were tested to unveil their bot score before a second human screening. A 2018 study looking to identify Twitter bots used the botomoter API with a threshold of 0.43 on a scale of 0-1, above which an account was considered a bot. The online tool uses a 0-5 scale, assuming the working limit, in this case, was 3.0. Looking at the Top 100 Tweeple, 78 looked like real users with a Botometer score below 3, whereas 22 were most likely bots. Those bot accounts roughly tweeted 240 times of the 16,509 tweets and were removed to make the data set consist of 16,269 tweets. Many of those rated high in Botometer ratings and are almost definitely bots. However, the tweets on the account are not relevant to this analysis. Based on these interaction levels, it's possible to identify several accounts spamming tweets and replies. However, the number of accounts with suspicious post frequencies is relatively tiny. These accounts primarily use automated scripts to post large amounts of hashtags containing trending keywords to boost the visibility of their tweets. 

Aside from the frequency of the posts, it is relatively difficult to identify these accounts as bots, especially as many need to start using the default profile, have profile pictures set, and often have reasonable follower counts. Their tweets, however, attract little attention, and a simple way to reduce the noise from these accounts is to filter out tweets that receive few or no retweets.  This removes some legitimate tweets, but it also gives a clearer picture of what a Twitter user would see and respond to when using the site. Given the reduction in apparent bots and spammers, Twitter's attempts to reduce the influence of these accounts have been successful. Much of the bot activity is now limited to nonsensical spam and is confined to a small proportion of users.

Much of the bot activity is now limited to nonsensical spam and is confined to a small proportion of users. Assuming Botometer is correct, with a score of 4.6, the top user, @ChirpyChet, is most likely to be a bot. The second user, @NutriciseOxford, Frances Carroll, scored 4.2 on the botomoter. Competing with ChirpyChet, is @raycasey2003, with a bolometer score of 4.6. Finally, @Simunch1 and @lesleyM25724591 scored an even 4.4.  @SallyMi83941850 scored 4.6, and @TheUKIsAMess scored the lowest, with a 3.9. The last user, @markzybay, scored a 3.1 on the bolometer and was considered a human. The large tweet-to-retweet ratio of the first two is a good indication of bucking the trend of the dataset. The top three accounts exhibit a high retweet rate but also wrote well-written responses to specific threads outside the dataset. This may indicate nearly indistinguishable bots or users with semi-automated accounts occasionally sign in and use themselves. The two most common self-attributed bots include QuentinCBot and PolyTwonkBot.


A total of 1.96% of people had their geolocation enabled for their tweets, a small figure, given the feature, is off by default. This may imply that the users actively opted out without blindly accepting the activation prompts by Twitter. A much more significant percentage of 67.16% of the users had enabled their location data, equivalent to 11,098 tweets. As per the data, 5096 users did not have locations, which is 30.86% of the total. As expected, the UK and England dominate the highest listed countries, with 3624 users listing their location as one of the two. Scotland proved the third-highest country with 790 tweets. Interestingly, some users from Washington D.C. and Ireland were included in the count. Furthermore, users were using the location box as a quiet protest against Brexit. Moreover, some of the EU member states have also contributed.

From the hashtags, mentions, and most used words, several different locations can be identified. There are larger groups based around political affiliations and small subsets often grouped around specific causes. From the most tweeted tweets and from analyzing the most used hashtags and mentions, there is a substantial anti-Conservative or, more specifically, anti-Tory group. Tweets that have received significantly more retweets and likes than others suggest that there lies dissatisfaction among the citizens of the UK. Looking at Twitter as a whole, the idea that Twitter users are pro-human rights can be identified. There is also a significant group promoting the #NHS hashtag, showing that many in the audience support the Nurses' Strike. This is further supported as most users self-report themselves to be in large cities, especially London, or other areas which voted in favor of the NHS. Large groups often stand out based on specific issues. One such group is supporting the failure of BREXIT as well as groups tweeting about the General Strikes and persistent inequalities in NHS and other organizations, showcasing that the people are aware and know how to stand for their rights.


The top two most common devices used to tweet were Android (37%) and iPhone (25%), making up 62% of the total tweets. The Web App made up 10%, leaving iPad (23%) and other devices (5%) to make up the remaining 27%. This highlights the need for journalists on social media to be mobile-friendly, as 4 out of 6 people were using mobile devices to tweet.


The most commonly used self-attributed bot was QuentinCBot, with 240 tweets. Other sources, including TweetDeck App, TweetCaster, and Twitterrific for iOS, were employed. Another self-attributed bot, PolyTwonkBot, was used to tweet 150 times.



The top hashtags reveal a lot about the underlying demographics of the Twitter audience. Many of the top 10 hashtags urged immediate leadership change and were anti-Conservative. Tags such as #ToriesOut, #FailedDemocracy, and #Brexit shows that much of the debate ultimately pooled around criticism of the Conservative Party and the Government. The prominence of the #ToriesFailedUK and #BrexitHasFailed hashtags also indicate the general political leanings of the Twitter audience and their opinion on Brexit. For clarity, the hashtag #GeneralElectionNow is removed from the charts below. To get a clearer picture of the conversation, it is also helpful to only look at the hashtags used, which received at least two retweets. Dismissing tweets that had no interaction removes prominent bot accounts and also the impact of users spamming hashtags. This process also removes some tweets from legitimate users. Still, it ultimately gives a more accurate picture of what someone using Twitter would see, as Tweets with more retweets are more likely to be seen and shared and are given more prominence in search results. While the overall picture is roughly the same between the two lists, there are some notable exceptions. Below are the top hashtags used, along with the percentage in which they appeared. Notably, these depict the citizens' demands. Once the tweets with low retweet counts are removed, specific hashtags disappear entirely. For example, the hashtags #ToryCriminalsUnfittoGovern and #ToriesMustGo are unrelated hashtags posted by spam accounts using the #GeneralElectionNow tag to give their tweets a higher chance of being seen in search results.


Overall, analyzing the tweets posted about the demand for general elections gives a good overview of how Twitter users view the political landscape in the U.K. The scope for debate is reasonably broad, but the discussion ultimately focused on specific topics and users, primarily dominated by anti-Conservatives. The analysis also demonstrates the rising importance of Twitter as a medium through which the commoner can directly raise concerns about the Government and communicate their discontent with the leadership without the filter applied by traditional methods of reaching a mass audience. Yet traditional media isn't entirely forgotten, and journalists and news organizations still play a vital role in contextualizing and summarising the debates and even in the vast, free. With rapid conversation online, respectful journalists are still seen as a reliable source of information and opinion on political matters.

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