Information School INF6000 Dissertation COVER SHEET (TURNITIN) Registration Number 160129287 Family Name Gruia First Name Mihaela Use of unfair means. It is the student's responsibility to ensure no aspect oF their work is plagiarised or the result oF other unfair means. The University’s and Information School’s advice on unfair means can be Found in your Student Handbook, available via http://www.sheFField.ac.uk/is/current. Assessment Word Count 14,995. If your dissertation has a word count that is outside the range 10,000 – 15,000 words or iF you do not state the word count then a deduction oF 3 marks will be applied Late suBmission. A dissertation submitted aFter 10am on the stated submission date will result in a deduction of 5% of the mark awarded For each working day after the submission date/time up to a maximum of 5 working days, where ‘working day’ includes Monday to Friday (excluding public holidays) and runs From 10am to 10am. A dissertation submitted aFter the maximum period will receive zero marks. Ethics documentation should Be included in the Appendix if your dissertation has Been judged to Be Low Risk or High Risk. ✓ (Please tick the box iF you have included the documentation) A deduction oF 3 marks will be applied For a dissertation iF the required ethics documentation is not included in the appendix; and the same deduction will be applied iF your research data has not been available For inspection when required. The deduction procedures are detailed in the INF6000 Module Outline and Dissertation Handbook. UK Politics and Internet Memes An Analysis of Theresa May Memes Shared on Twitter during the 2017 General Election A study submitted in partial fulfilment of the requirements for the degree of MSc Data Science at THE UNIVERSITY OF SHEFFIELD by MIHAELA GRUIA 4 September 2017 1 ACKNOWLEDGMENTS One spring afternoon, on the 23rd of March 2016, I met Dr Farida Vis in the Diamond Cafe. I was fascinated by her work and our conversation opened my mind to the prospect of completing the Data Science MSc in the Information School. It was a serendipitous encounter that helped kickstart a wonderful journey. Nearly 3 months later, on the 29th of June, I received a letter from Catherine McKeown from the UoS Finance Department that I had been awarded a Postgraduate Scholarship. Completing this course would have not been possible without that fortunate encounter, and without the scholarship. Thank you from the bottom of my heart. Although it often left me frustrated, tired, and confused, this dissertation has also tested my new ninja data science skills, and allowed me to use old knowledge that I gained in my Politics & IR BA. I can honestly say that this been my favourite project to work on in my entire academic career. Thank you Farida for the all the office hours and ‘walk-and-talks’ to the train station, as opportunities to reflect and refine my ideas. I have always admired you, your energy, your ability to multitask on top of multitasking, and coming out at the end of it being the superwoman that you are. The photos of Miss Finn have been the delight of my days when I needed that small push to keep coding. Thank you Peter for agreeing to be my second supervisor during the crucial last month of August and for reading my work and refining it in the process. Hannah and Chris, you are the best second coders any social media researcher could ever ask for, thanks for the attention to detail and patience to go through my spreadsheets and code frames. I owe you one so make sure you redeem it. Gemma, thanks for coming in for the ‘dissertation-work-sessions’, we may not have been the most productive, but we kept each other going and supported one another. In a rather awkward way, thank you to the Prime Minister for providing us with such interesting visual material for study... Lastly, a special thank you to my better half, Garrett, for listening me talk about memes of Theresa May for hours on end, for engaging with my work and showing interest in all of it, despite it often being the opposite. I feel proud that you now know what a GIF is. 2 Structured Abstract Background Theresa May has been the centre oF political controversy since she became UK Prime Minister (in July 2016). An especially controversial decision was her calling a snap election in April 2017, for a General Election (GE) to be held in June 2017. Given the increasing use oF social media to maniFest political views, images play a signiFicant role in the ways in which political views are expressed. Memes and GIFs in particular are a subset oF images that increasingly play an important role in how voters comment on politics on platForms like Twitter. Aim The overall aim oF this dissertation is to better understand the ways in which Twitter users utilised memes and GIFs during the 2017 GE to respond and engage with Theresa May’s campaign and perFormance as a politician and speciFically, as a Female politician. In particular, three case studies were selected, each corresponding to a key aspect oF her campaign during May and June 2017: strong and stable (SAS), weak and wobbly (WAW) and TheresaMayGIFs (TMG). The aim was to understand what was depicted in the memes, who shared the memes and what was the stance oF the memes with regard to Theresa May’s campaign. Methods The dataset consisted oF the 300 most popular shared tweets containing images (100 per case study) and they were collected using Pulsar, a commercial analytics tool. Several social media research methods were employed in the analysis oF the memes, namely content analysis oF image and text, actor type analysis and sentiment analysis of image and text. Specific code frames were developed For each method and second coders were trained to reFine the accuracy of each code frame. The Intercoder Reliability scores of the code frames were reported using Scott’s Pi coeFFicient. Results First, Theresa May’s policies and maniFesto were revealed to be the most reFerred to issues by posters in two oF the three case studies (SAS and WAW), while Theresa May’s TV perFormance was the most Frequent issue in the TMG case study. Second, members oF the public were the most active category oF Twitter user in two oF the three case studies (WAW & TMG), and For the third case study it was political actors (SAS). Lastly, when analysed both in isolation of the text as well as alongside 3 the text, all case studies revealed that the memes were critical oF Theresa May directly or oF an aspect of her campaign. Discussion These Findings suggest the Following implications. First, memes can have a political orientation, and this has the potential to be negative. Second, these Findings suggest that memes are a powerFul Form of political expression and that it is diFFicult For parties to control the narrative online. Third, political parties could consider more clever ways and be savvier when it comes to ways in which they deal with Twitter and memes. Lastly, traditional Forms oF political campaigning and communication, which may imply repeating the same line or slogan over and over again, are not efFective when it comes to certain social media avenues, and particularly Twitter and memes. 4 TABLE OF CONTENTS Chapter 1. Introduction and Context pp 7-11 Chapter 2. Literature Review pp 12-20 2.1. Politics and Social Media pp 12-15 2.2. Visual Representation of Female Politicians pp 15-16 2.3. Memes 2.3.a. Conceptual DeFinition of Memes pp 16-17 2.3.b. Memes and Politics pp 17-19 2.3.c. Memes and British Politics pp 19-20 Chapter 3. Methodology pp 21-34 3.1. Case Studies and Visual MotiFs pp 21-28 3.1.1. Strong and Stable 3.1.2. Weak and Wobbly 3.1.3. TheresaMayGIFs 3.1.4. Maybot 3.1.5. Laughing Theresa May 3.2. Methods pp 28-31 3.2.1. Method 1 Content Analysis of Image and Text 3.2.2. Method 2: Actor Type Analysis 3.2.3. Method 3: Sentiment Analysis of Image and Text 3.2.4. Note For All Methods: Intercoder Reliability 3.3. Data Collection Strategy pp 32-34 3.3.a. Tools and Steps to Retrieve Data 3.3.b. Search Terms 3.3.c. Sample Size 3.3.d. Data Storage and Security 3.3.e. Ethics Chapter 4. Findings, Analysis and Discussion pp 35-63 4.1. Findings: Strong and Stable pp 35-43 4.1.a. Content of Memes 5 4.1.b. Posters oF Memes 4.1.c. Evaluative Stance of Memes 4.1.d. Strong and Stable: Discussion 4.2. Findings: Weak and Wobbly pp 43-50 4.2.a. Content of Memes 4.2.b. Posters of Memes 4.2.c. Evaluative Stance of Memes 4.2.d. Weak and Wobbly: Discussion 4.3. Findings: TheresaMayGIFs pp 51-61 4.3.a. Content of Memes 4.3.b. Posters of Memes 4.3.c. Evaluative Stance of Memes 4.3.d. Weak and Wobbly: Discussion 4.4. Comparing Case Studies pp 61-64 Chapter 5. Conclusion pp 65-68 5.1. Review of Research Questions and Research Objectives – Summary oF main conclusions and Findings 5.2. Limitations of the Research 5.3. Suggestions For Future Research References pp 69-74 Appendix 1 pp 75-77 Code Frame 1 RQ1 Appendix 2 pp 78-80 Code Frame 2 RQ2 Appendix 3 pp 82 Code Frame 3 RQ3 Appendix 4 p 82 [Attached document] Ethics Approval Letter Appendix 5 pp 83-84 [Attached document] Access to Dissertation Form Appendix 6 pp 85-87 [Attached document] ConFirmation of Address Form 6 Chapter 1: Introduction and Context Despite taking charge oF the UK at one oF the most turbulent times in recent political history given the Brexit vote, Theresa May Faced intense criticism that she did not hold a legitimate mandate to be Prime Minister (PM).