Blog Post 1 — About me

My name is Alma, and currently I am a student in Royal Holloway University of London. Originally, I am from Kazakhstan, and have never been to the UK before coming to the university. I am currently on my third year, studying BSc Business and management degree. I believe that this very broad degree allows me to individually choose the modules I will be studying that would perfectly fit me and my preferences, as at the beginning I had no clue of what exactly I want to study. Therefore, the modules I have now, and had in the previous years are all very different, and are in different pathways. As an example, this year I have chosen Social media, networks and business module from the digital innovation pathway, in combination with marketing and entrepreneurship pathways.

I am very interested in working in HR, marketing of audit sector as they are the most suitable for me, considering that I already had a work experience on the summer 2019 in Deloitte LLP as an audit assistant. Apart from that, I had another work experience in the summer 2016 in ‘Kazlegprom-Almaty’ LLP in Kazakhstan as a marketing assistant. I truly enjoyed both of the jobs and I might consider to return to one of the companies after the graduation.

Apart from that, I am interested in learning languages. Currently I am fluent in 3 languages, which are Kazakh, Russian and English, and also learning French. I believe that the knowledge of languages, in combination with a high quality education and a Bachelors degree from one of the very good universities in the UK would give me a competitive advantage when applying for a job.

Blog post 2 — Fake news and misinformation

Fake news are the spread of false information within the media, of which one of the reasons could be to fulfill the political agenda. Current blog post will focus on fake news that have political implications, especially paying an in depth attention to the 2016 US presidential elections. The popularization of fake news in social media has largely started in 2016 by the current US President Donald Trump. According to the Guardian, the last weeks of the US elections the social media — Facebook and Twitter in particular were full of false information about Trump and Clinton. For example, one of the websites posted a false article about Hillary Clinton selling weapons to Isis, creating a major debate. Also, website wtoe5news.com reported that Pope Francis had endorsed Trump’s presidential candidacy (Allcott and Gentzkow, 2017), creating a controversy among the voters.

The social media was so overcrowded by false news, that in the Buzzfeed News report it was stated that the fake news actually outperformed the legitimate news in terms of likes and shares within the media platforms (Debatingmatters.com, 2016). In fact, Buzzfeed analytics found out that top 20 false news on the US elections generated 8.7 million shares and comments on Facebook (Watson, 2016). As a result, the amount of fake news on this topic became so large that it influenced real people to vote for Trump, showing how quickly and easily fake news on social media can change peoples minds and influence their decisions. 

The spread of fake news has become so fast and significant due many reasons. Fake news usually have very controversial and eye catching headlines, and one of the problem of the spread of fake news is that users share articles in social media based on the headings, without even reading the whole article (Woolf, 2016). However, fake news are more oftenly spread by bots and trolls. Bots are computer algorithms created by humans to carry out various tasks repetitively. Bots are used to propagate fake news and increase their popularity on social media. They propagate false news due to their ability to retrieve not validated (or non-curated) information on the Internet. Bots post continuously on social media, thus spreading non-curated news and information using hashtags to reach the wider audience (Cits.ucsb.edu, 2019). Therefore, bots spread fake news by continuously tweeting them, or comment those news on the real users’ posts.

Trolls are another widespread way of propaganda fake news. Trolls are humans who own fake accounts on different social media platforms with a purpose to post comments that insult or argue with real users. Similarly to bots, trolls also post comments with fake news continuously to gain the actual users’ attention.  

Social media platforms are under pressure of the users, who suggest that news are being filtered before posting on major social media websites like Facebook and Twitter. Thus, according to (Allcott and Gentzkow, 2017), As can be seen on the graph, the graph on the top represents referral sources for the top 690 US news sites, whereas the graph on the bottom represents  web traffic sources for a list of 65 major fake news sites. For top news cites social media represents only 10% of total traffic, whereas fake news websites post 41.8% of their news on social media platforms, which makes these platforms less reliable than direct browsing.

Fake news can negatively impact social media platforms, as users could stop relying on the articles they found on social media, thus decreasing the reputation and importance of news cites within social media. Therefore, to solve that Facebook and Google announced that they will make it harder for fake news to spread and earn money from their advertising networks.

References

Allcott, H. and Gentzkow, M. (2017). Social Media and Fake News in the 2016 Election. Journal of Economic Perspectives, [online] 31(2), pp.211-236. Available at: https://pubs.aeaweb.org/doi/pdfplus/10.1257/jep.31.2.211 [Accessed 10 Jan. 2020].

Cits.ucsb.edu. (2019). How is Fake News Spread? Bots, People like You, Trolls, and Microtargeting | Center for Information Technology and Society — UC Santa Barbara. [online] Available at: https://www.cits.ucsb.edu/fake-news/spread [Accessed 10 Jan. 2020].

Debatingmatters.com. (2016). “Social media sites should filter out fake news” – Debating Matters. [online] Available at: https://debatingmatters.com/topic/social-media-sites-should-filter-out-fake-news/ [Accessed 10 Jan. 2020].

Watson, T. (2016). Tom Watson: ‘Fake news’ is changing the way we see the world. [online] The Independent. Available at: https://www.independent.co.uk/voices/fake-news-facebook-twitter-social-media-sharing-changing-way-see-world-face-up-to-it-labour-tom-a7431466.html [Accessed 10 Jan. 2020].

Woolf, N. (2016). Obama is worried about fake news on social media – and we should be too. [online] the Guardian. Available at: https://www.theguardian.com/media/2016/nov/20/barack-obama-facebook-fake-news-problem [Accessed 10 Jan. 2020].

Blog post 3 — Social network analysis using Gephi

I used the Gephi software to analyse the hashtag #GoldenGlobes2020 on Twitter. As it can be seen from the screenshot, there are 47 Nodes, which represent people who mentioned, tweeted, quoted or retweeted the hashtag #goldenglobes2020 in Twitter. The nodes are colored in 7 different colors based on the modularity class. Modularity measures the strength of division of a network into clusters (or communities). 7 colors mean that there are 7 clusters, and we can see that the purple color is the most often one on the diagram, therefore from the Data Laboratory we can see that the most frequent action by users was mentioning the hashtag. Therefore, the purple Nodes show users who mentioned. The size of the Nodes is ranked based on Betweenness Centrality with a minimum size of 1 and a maximum size of 21.5. However all Nodes have an equal size of 21.5 because betweenness centrality is equally spread. 

 Edges, on the other hand are the lines which connect the Nodes. There is a total of 64 Edges, and we can see that there can be multiple Edges coming from one Node, which means that when someone in Twitter takes an action with the hashtag, other people see it and then can retweet or comment. Therefore, Edges show the interaction between Nodes. All Edges are in green color as they are ranked by weight, and all of them have an equal weight. 

My Gephi diagram does not have a central point from which the other Nodes and Edges come from because I am analysing a hashtag, instead of an account. Therefore, it is not possible to have hashtag as a central point because people tweet or comment it on different accounts that do not necessarily overlap.

Blog post 4 — Reflection on my learning experience

I really enjoyed the MN3536 module, as all the material covered in classes was interesting and new form me, so I did not miss any lectures, and tried my best to attend all the workshops. For me, one of the most interesting and overwhelming topics was Networks and ranking Algorithms. The question of what happens when we click the search button in Google, and how Google decides which page to display first was a mysterious question that I did not had an answer to before choosing this module. I was very excited and positively surprised by the amount of useful and interesting information I have an access to after completing this module. As for example, now I know that when I click a search button in Google, I am not actually searching the web, instead, I am searching the Google’s index of the web. This is done by using the software programs called ‘spiders’. First, spiders fetch few web pages, then they follow the links on those pages and keep fetching, until we index a big chunk of the web of billions of pages stored across many thousands of machines. Thus, when making a Google search, Google looks for the specific search across millions of pages and analyse them according to the number of times a specific word searched appears in the text, or whether it appears on headings, and how reliable the website is. After that, we get the results with pages that Google finds to suit our search the most, however all of that is done in less than few seconds. Also, now I know that while living in a highly networked world, there are such aspects like Networked behaviours and Network structure, which include network effect that can be positive or negative, and network externality. 

Another topic that interested me was about fake news and misinformation. While doing some research for the assignment, I was impressed how quickly and easily false information can spread around the web, and that fake news have political implications. Before this module I never questioned how and why fake news spread. However, now when I know that, I still find it mind blowing how bots, trolls and cookies are involved to spread fake news and even shift people’s attention from some news or events using false information. Knowing how fake news spread through the web will indeed be useful for the future studies and work experience. Now, when I know that news articles on social media can be false and are not always filtered, I would not majorly rely on them in the future to prevent any mistakes and misinterpretations of the information.

Overall, I enjoyed this module for the amount of readings, external links like videos, and the assignments we were given. I personally enjoyed the group assignment where we had to write a report and evaluate social media presence of the company. This assignment taught me to use different external tools like Keynote or Trackalytics to analyse and evaluate social media of our chosen company in depth.

I think that the knowledge of how to use social media analytics tools would be especially useful when working in marketing area — especially for analysing target audience and potential target groups via social media. This would give a broader understanding for companies of who to hire for an advertising campaign from celebrities or highly influential people. Thus, having true expectations on what target groups would be captured, and the extent to which social media would be a useful tool for advertising campaigns and marketing.

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