This article discusses the use of the hashtag #Brexit on social media to express opinions and engage in debates about the United Kingdom's exit from the European Union. It explores the role of celebrities, politicians, and everyday citizens in shaping public opinion and the potential impact on the political landscape.
Contribute Materials
Your contribution can guide someone’s learning journey. Share your
documents today.
Brexit Opinion Hashtag1 BREXIT OPINION HASHTAG IN 2019 By (Name) The Name of the Class (Course) Professor (Tutor) The Name of the School (University) The City and State where it is located The Date
Secure Best Marks with AI Grader
Need help grading? Try our AI Grader for instant feedback on your assignments.
Brexit Opinion Hashtag2 Brexit Opinion Hashtag in 2019 Introduction The twitter hashtag being considered here is with regard t the issue of Brexit that started back in June of 2016 when a majority of United Kingdom citizen voted in the referendum on leaving the European Union. The exit from the European Union (EU) was supposed to be finalized by the 29thof March 2019. However, due to political disagreements and postponing the matter is expected t be finalized by the end of October 2019. In spite of the finality of the matter European people are still sharing their opinion on Brexit over social media using the hashtag Brexit (#Brexit). The hashtag Brexit is also used with either one of the following “#leave or #remain”. People are using the social networking platform as an avenue to express their opinion, and inquiry on the opinions of major celebrities e.g. David Beckham. Some of these celebrities have responded to tweets and shared with the public with regard to how he/she voted with regard to the United Kingdom leaving the European Union(AFM, 2017). According to most people it is definitely safe to say that Brexit has been a driver in numerous political and socio-economic debates since the year 2016. Ever since the 2016 referendum protagonists and supports of Brexit has used the defense “It is the will of the people” to support the reason as to why the United Kingdom should exit from the EU. Nevertheless, the difference between what people voted for in terms of Brexit and the consequences of such an action still remains murky waters that spark heated debates on social media platforms. The situation has been largely fueled by the differing opinion of UK politicians who has supports on both sides and are therefore, making critical choices when they decide t support one side and oppose the other. Hence, the differences in opinion on the Brexit matter will cause political disruptions that will forever affect the political landscape of the UK going forward(Menon, 2019). Background Information
Brexit Opinion Hashtag3 In any nation that prides itself on the presence of democratic rights for all stakeholders, the expression of public opinion still remains the chief determinant of democratic politics. Some political analysts do however worry that paying considerable attention on matters of public opinion could result in numerous issues being viewed on the basis of plebiscite; this is dangerous because the members of parliaments will make legal and constitutional amendments and cite them to be the “will” of the people of the United Kingdom. This form of legitimization of public opinion is not the true form of democracy but a distortion that seeks to validate democracy and social well-being of a people based on their overall participation in matters of policy-making. Most twitter users believe that the Brexit issue will result in considerable changes to the electorate process in the UK. According to Bobby Duffy highlighted the improper nature of Brexit related debates and the limited depth with which they were discussed by the public. Similarly, Noah Carl noted that most UK citizens demonstrated “motivated reasoning. Where they expressed their opinion on Brexit with regard to the option that most conformed with their psychological beliefs. Lastly, researchers could find no evidence to suggest that individuals in favor of remaining were more informed that their leave counterparts(Menon, 2019). Individuals like Paula Surridge warned against political party based reasoning where a person’s Brexit decision was wholly influencers by left and right political divisions in the United Kingdom politics. Paul observed that traditional political divides shared mixed opinion on the issue of Brexit and it was therefore unrealistic to consider the opinion of either political party or group. Moreover, Surridge challenges voters to critically evaluate the relationship between political behavior and personal values with regard to the voting issue in question. Nevertheless, Evans and Shaffners observed that people were more inclined towards the issues of Brexit on an individual level; so much so that they were unwilling to be swayed by political party loyalties. The researchers indicates that the intensity and emotion associated with Brexit was strong enough to overwrite party based voting behavior is a fair percentage of the UK population. Moreover, Brexit identities caused UK voter to shift their glaze in the context of how they discern the world around them. Hobolt and Tilley’s suggested that people of either side of the Brexit debate were interpreting new information in a manner that conforms with their personal beliefs; as a result, they were unwilling to see the true provided by the new information. Bobby
Brexit Opinion Hashtag4 Duffy also spoke on these when he remarked on the limited role played by new facts in the shaping of opinions held by people on the opposing sides of the Brexit debate(Menon, 2019). According to Richards and Heath, the Brexit issue is the only cause of division for nations within the United Kingdom with strongholds developing for remain and leave supporters. For example places like Stoker and Jennings were filled with remain supporters; even thou a majority of them had very limited understanding of the socio-economic and political implications associated with their remain decision. Between 2016 and 2019 researchers realized that public opinion on the matter of Brexit was considerably more subtle and volatile than most people considered it to be. For example, a person could change his/her stand on the matter based on the opinion of a friend, politician, or celebrity without looking at the facts. As a reason, many people have unnecessary change their opinion over the past two years simply because of the desire to conform(Menon, 2019). Twitter Hashtag Role The information shared across twitter with the Brexit hashtag was used by many young people aged between 25 and 35 as factual information that was reliable enough to mold their opinion and the way the decided to vote. Politicians were viewed by many to pose considerable understand of the issues and a result the people on twitter gravitated towards individuals in public offices who shared their opinion on Brexit. The main evil associated with this form of fact gathering is the biased inclination towards what conforms with my personal expectation and avoidance of information that challenges pre-existing misconceptions about the implication of Brexit. The data used in this assessment has over 1 million tweets shared by European people in the matter of Brexit. Some of the tweets are from prominent people who do not reside in the United Kingdom like Donald Trump and Hilary Clinton. This data was retrieved from Kaggle online data archives; which is a reliable source for big data (i.e. both qualitative and quantitative). It is clear that just by randomly sampling a few of the tweets we can see a significant percentage of the people on Twitter are interested on getting the opinion or others. Moreover, the
Paraphrase This Document
Need a fresh take? Get an instant paraphrase of this document with our AI Paraphraser
Brexit Opinion Hashtag5 tweets are highly emotional providing a clear mental imagine of the participant’s point of view (Kemp, et.al. 2018). Majority of the tweets assess the issue of Brexit with regard to how remaining or leaving will affect the following areas of government and everyday life: The equitable distribution of wealth in the UK; the enforcement for laws that govern both the rich and poor without favorism; the implementation of strong trade unions that will protect the right of workers in the UK; and the expansion of the private sector as a way to better the UK economy. On the other hand, some of the tweets are also misguided due to a lack of knowledge and ignorance. Majority of the tweets that are misleading are from young people who don’t clearly understand the role played by the EU and the implications associated with the UK nations leaving. There are no signs of Censorship in the tweets because there are both positive and negative tweets on the issue of Brexit. Censorship is observed when data is seen to be overwhelming positive, neutral, or negative. The drawback of censorship is the distortion of public opinion leading to the formulation of wrong conclusion. Therefore, sensitivity analysis is an important was to determine that the Brexit hashtag tweets are a genuine representation of the opinion of UK citizen and stakeholder(Pillai, 2015). Sensitivity Analysis Sensitivity analysis is a critical aspect of decision making that shapes the understanding of individuals. This technique seeks to assess how sensitive a given decision is to the alteration of one or more values in the assessment. The major limitation of sensitivity analysis is the impossibility of examining all the possible alterations and combinations associated with all the variables. The analysis of the qualitative data in Rstudio involves the assessment of the data with regard to where the opinion of Brexit was influenced by the popularity of the person tweeting about the subject. The sensitivity analyses in Rstudio revealed that majority of the positive tweets were posted by celebrities, politicians, and influential members of society. On the other hand, a majority of the negative tweets on Brexit were championed by everyday UK people with strong opinions on the issue of Brexit. Overall we can state that majority of the negative emotion associated with Brexit was directed
Brexit Opinion Hashtag6 towards individuals holding public office in the United Kingdom (Iooss and Saltelli, 2017). The over 1 million tweets were categorized into three categories i.e. positive tweets, negative tweets, and neutral tweets. Lastly, a column was added for support of Brexit. All four columns were populated with either 1’s or 0’s depending on whether the title statement was fulfilled. For instance, support for Brexit was offered a value of 1 if the tweet was in support of the UK leaving the European Union. The data was then uploaded into Rstudio for Sensitivity assessment. The sensitivity analysis centers on establishing whether or not the negative, positive, and neutral tweets have a relationship with how people fell about Brexit. For example, does a negative tweet have a 70% chance of being in support of Brexit? In the sensitivity analysis we will use a multiple-linear regression approach to demonstrate the relationship between tweets and the support for Brexit. Since, the value of support for Brexit will assume values between 0 and 1; we will take the results to be probabilistic in nature. Therefore, a value of 0.976 associated with a positive tweet means that there is a 97.6% chance that that particular positive tweet is in support of Brexit. From the sensitivity analysis it is was clear that positive tweets were largely for high support for Brexit. While Neutral tweets were responsive for a small percentage of support for Brexit. Negative tweets were mostly off topic and therefore were considered irrelevant to the assessment. The regression and sensitivity results are presented below: l m( f or mul a = `Suppor tBr exi t `~ Posi t i ve + Neut r al+ Negat i ve) Resi dual s: Mi n1QMedi an3QMax - 0. 16080. 00000. 00000. 00000. 8392 Coeffici ent s:( 1 notdefi ned because ofsi ngul ar i t i es) Est i mat eSt d.Er r ortval ue Pr ( >| t | ) ( I nt er cept )1. 608e- 013. 588e- 04 448. 231< 2e- 16 * ** Posi t i ve8. 392e- 011. 513e- 015. 546 2. 93e- 08 * ** Neut r al- 2. 871e- 151. 513e- 010. 0001 Negat i veNANANANA - - - Si gni f .codes:0 ‘ * ** ’0. 001 ‘ ** ’0. 01 ‘ *’0. 05 ‘ . ’0. 1 ‘’1 Resi dualst andar d er r or :0. 1513 on 1048422 degr eesoff r eedom ( 150 obser vat i onsdel et ed due t o mi ssi ngness) Mul t i pl e R- squar ed:0. 8125,Adj ust ed R- squar ed:0. 8125 F- st at i st i c:2. 271e+06 on 2 and 1048422 DF,p- val ue:< 2. 2e- 16
Brexit Opinion Hashtag7 > konf ound( Sent , Posi t i ve) Per centBi asNecessar yt o I nval i dat e t he I nf er ence: To i nval i dat e an i nf er ence,64. 725% oft he est i mat e woul d have t o be due t o bi as. Thi si sbased on a t hr eshol d of0. 296 f orst at i st i calsi gni fi cance ( al pha = 0. 05) . To i nval i dat e an i nf er ence,678596 obser vat i onswoul d have t o be r epl aced wi t h cases f orwhi ch t he eff ecti s0. I mpactThr eshol d f ora Conf oundi ng Var i abl e: An omi t t ed var i abl e woul d have t o be cor r el at ed at0. 059 wi t h t he out come and at 0. 059 wi t h t he pr edi ct orofi nt er est( condi t i oni ng on obser ved covar i at es)t o i nval i dat e an i nf er ence based on a t hr eshol d of0. 002 f orst at i st i calsi gni fi cance ( al pha = 0. 05) . Cor r espondi ngl yt he i mpactofan omi t t ed var i abl e ( asdefi ned i n Fr ank2000)mustbe 0. 059 X 0. 059 = 0. 003 t o i nval i dat e an i nf er ence > konf ound( Sent , Neut r al ) Per centBi asNecessar yt o I nval i dat e t he I nf er ence: To sust ai n an i nf er ence,100% oft he est i mat e woul d have t o be due t o bi as.Thi si s based on a t hr eshol d of0. 296 f orst at i st i calsi gni fi cance ( al pha = 0. 05) . To sust ai n an i nf er ence,1048425 oft he caseswi t h 0 eff ectwoul d have t o be r epl aced wi t h casesatt he t hr eshol d ofi nf er ence. I mpactThr eshol d f ora Conf oundi ng Var i abl e: An omi t t ed var i abl e woul d have t o be cor r el at ed at0. 044 wi t h t he out come and at 0. 044 wi t h t he pr edi ct orofi nt er est( condi t i oni ng on obser ved covar i at es)t o sust ai n an i nf er ence based on a t hr eshol d of0. 296 f orst at i st i calsi gni fi cance ( al pha = 0. 05) . Cor r espondi ngl yt he i mpactofan omi t t ed var i abl e ( asdefi ned i n Fr ank2000)mustbe 0. 044 X 0. 044 = 0. 002 t o sust ai n an i nf er ence.
Secure Best Marks with AI Grader
Need help grading? Try our AI Grader for instant feedback on your assignments.
Brexit Opinion Hashtag8 References AFM, A. 2017.Balanced decision-making: Dealing with blind spots. Case study with management boards of small and medium sized banks. Retrieved March 20, 2019, from AFM [Dutch Authority for the Financial Markets]: <https://www.afm.nl/en/professionals/onderwerpen/gedrag-cultuur-publicaties> Iooss, B., & Saltelli, A. 2017.Introduction: Sensitivity Analysis.Chatou, France. Kemp, S. E., Hort, J., & Hollowood, T. 2018.Descriptive Analysis in Sensory Evaluation.Hoboken, NJ: John Wiley & Sons. Menon, A. 2019.Brexit and public opinion 2019.London: The UK in a Changing Europe. Pillai, N. V. 2015. Data Analysis and Interpretation.Conference: Presented to the participants of an Induction Training Programme organized by the Institute of Management in Government in collaboration with DoPT, Government of India(pp. 1-31). Thiruvananthapuram, India: Centre for Development Studies.