Reflection Topic 2 – ‘Fake News’: Who is responsible?

My initial stance regarding the problem of fake news was slanted towards placing all responsibility on the individual for assessing whether information online is false or not. However reading Luke’s comment allowed me to reflect on just how impractical it is for the average reader to critically assess each article or story they read to judge its validity, due to the amount of time it would take. Luke also linked a recent study from the BBC which pointed out how fake news spreads faster and to a much wider network then genuine news, (Kleinman 2018).  Additionally reading Luke’s blog also made me aware of just how important data literacy skills are in addition to traditional media and digital literacy skills. Reading Luke’s blog prompted me to do some more research into data literacy. In my comment replying to Luke I referenced a study which showed just how easy It is to manipulate data, particularly for private companies who want to make their profit margins appear to be more reliable and consistent over time (Kwapien,2015).

Reading Jermey’s blog also prompted me to re-assess my position of placing all the responsibility on the individual. In his blog post Jeremy linked to a Washington post article about Twitter executives refusing to take action to stem the flow of fake news being spread on their platform (Borchers, 2018). This led me to doing some further research into how social media platforms address the issue of fake news. In my comment replying to Jeremey I referenced how Zuckerberg, changed his stance on fake news, from ignoring the problem to later focusing on developing a solution.


Reflecting on false information online has allowed me to see just how widespread the problem is, and how impractical it is to expect individual readers to take all the responsibility for assessing it. Below I created a diagram to expand upon how different actors are responsible for fake news online.

Leading a healthy lifestyle infographic (2)

Word Count: 325


Borchers, C. (2018). Analysis | Twitter executive on fake news: ‘We are not the arbiters of truth’. [online] Washington Post. Available at:[Accessed 19 Mar. 2018].

Kleinman, Z. (2018). Fake news ‘travels faster’, study finds. [online] BBC News. Available at:[Accessed 19 Mar. 2018].

Kwapien, A. (2015). Misleading Data Visualization Examples. [online] BI Blog | Data Visualization & Analytics Blog | datapine. Available at: [Accessed 19 Mar. 2018].


Brave New World – Developing the skills for evaluating “Fake News”

False information published online can be designed to further a political agenda, or simply to generate revenue through misleading titles, article descriptions and media in the form of “clickbait”. “Information gap theory” offers some insight into why clickbait is successful, when a reader sees a snippet of a fake news article they will draw upon their background knowledge of that subject (Golman and Loewenstein 2015).This leads to a desire for the reader to seek out the gaps in information from the snippet itself thereby motivating the reader to click the full article (Golman and Loewenstein 2015).

With regards to political agendas, social media posts crafted by fake accounts can be used in order to push a certain narrative that is factually untrue, (BBC News, 2017). Likewise bots can be used to share factually inaccurate tweets in order to make the tweets themselves appear more credible by propagating them into a wide network where they are seen by many people (Wooley, 2016).

bbc fake news post
Example of fake news article on Facebook. Source: BBC News. (2017). Russia posts ‘reached 126m Facebook users’. [online] Available at: [Accessed 6 Mar. 2018].
To assess the validity of this false information online, technological interventions can be used. For instance by using online tools such as “news tracer” users can determine the factual accuracy of a tweet by analysing who shared it, and if the claims in the tweet have been verified by others within the network (Keohane et al., 2017).

(Van Dijk, & Van Deursen, 2010) argued that the critical thinking skills required for traditional media literacy are not enough when it comes assessing information online. This is in part due to the sheer wealth of options for sources of information available on the web, so new digital skills are needed to evaluate them. An example of one of these digital skills is “strategic skills” which is needed in order to develop a goal for what information you are hoping to find and for the method you will use to find this information (Van Dijk, & Van Deursen, 2010). More details on digital skills are in the diagram below.

Evaluating information online


Word Count: 300


BBC News. (2017). Russia posts ‘reached 126m Facebook users’. [online] Available at: [Accessed 6 Mar. 2018].

Golman, R. and Loewenstein, G. (2015). Curiosity, Information Gaps, and the Utility of Knowledge. SSRN Electronic Journal. [online] Available at:

Keohane, J., Vogelstein, F., Geltzer, J., Eden, S., Simonite, T., Gendreau, H. and Finley, K. (2017). WHAT NEWS-WRITING BOTS MEAN FOR THE FUTURE OF JOURNALISM. [online] WIRED. Available at: [Accessed 6 Mar. 2018].

Woolley, S. (2016). Automating power: Social bot interference in global politics. [online] Available at: [Accessed 5 Mar. 2018].


Reflecting on Digital Divides

Dom left a comment on my blog posing a question as to whether the digital divide will grow or decrease. This caused me to reflect on my blog post and extend it by posing another question which is to the extent that digital inequalities are either simply mirrored or reproduced through the web? I briefly answered this question with an example from my personal experience, by thinking about this comment I was effectively able to extend my original answer and this also helped when it came to replying to Luke’s blog. With this newly formulated question in mind I looked up Sharma and Brooker’s paper regarding racism denial on twitter and set about answering the question that I had come up with to see if the web was widening the digital divide (2016). I then was able to relate this new knowledge to what Luke had written.

Sharing my knowledge on Phoebe’s blog led to a reply with a link to an ONS report on internet use as it relates to factors such as age. This was a theme that had I discussed in my original blog post. However this study suggested that inequalities may be shrinking as the number of people aged 65-74, and 75+ increased significantly between 2011 and 2017 (see Figure 2 below).

Figure 2_ Recent internet use in 2011 and 2017 by age group, UK

As for the development of my digital skills, I found that creating a diagrammatic representation of what I identified to be the dominant groups in online spaces based on the two papers I cited in my original blog post allowed me to visualise and therefore to think about the research in a more direct and engaging way then I am used too. Additionally by making the diagram it made me think about the similarities between the two papers more so then simply writing would have.

Word Count: 300


Dom’s comment on my Blog:

Comment made on Luke’s Blog:

Comment made on Phoebe’s Blog:

My original Blog post: (2018). Internet users in the UK – Office for National Statistics. [online] Available at: [Accessed 5 Mar. 2018].

Sharma, S. and Brooker, P. (2016) ‘#notracist: Exploring racism denial talk on Twitter’ in Daniels, J. et al (Eds) Digital Sociologies


Digital Divides: Who Controls the Web?

Digital differences refer to the diverse experiences people have on the web depending on factors such as their age, gender and ethnicity. In order to explore this I will look at existing research (summarized in fig 1 below), before concluding with some thoughts on how digital differences impact the way I learn online.

dominiant groups in online spaces

Lutz, & Hoffmann, (2017) in their analysis of user’s online participation found several potential areas for a digital divide, including a split based on user participation based on political beliefs and age. Research indicated that there existed a divide between those of more extremist political beliefs espousing them online, and those of more liberal beliefs who self-censored in an effort to avoid confrontation and online bullying (Lutz, & Hoffmann 2017). Also whilst elderly people were found to want to access online spaces they lacked the technical abilities to do so (Lutz, & Hoffmann 2017).

Other studies have also looked at the web from an intersectionality perspective to see the differing experiences caused by gender, race and class (Nakamura, 2007). Studies on online spaces such as LambaM00 pointed to denial of forms of racial discourse in order to secure and reflect the values of the predominately white and middle class user base (Nakamura, 2007).

The ways in which I learn online are impacted in part by educational level and digital literacy. The University provides access to various academic resources which allow me to draw from a range of research materials to gain deeper understandings to certain topics that would be usually be hidden by academic journal paywalls. As for my economic status and country of origin, middle class, & British citizen this gives me the socio-economic ability to access the web which is largely catered towards English speaking people. Conversely people in poorer parts of India and certain other places in the World will have no ability to access the web.

Word Count: 300



FutureLearn. (2018). Page from Learning in the Network Age – University of Southampton. [online] Available at: [Accessed 22 Feb. 2018].

Lutz, C., & Hoffmann, C. P. (2017). The dark side of online participation: exploring non-, passive and negative participation. Information, Communication & Society, 1-22.

Nakamura, L. (2007) ‘Race in/for cyberspace: identity tourism and racial passing on the internet’ in Bell and Kennedy (Eds) The Cybercultures Reader (Second Edition only) London, Routledge.

Introductory Topic – Reflection

Answering the introductory topic about digital residents and natives was more challenging then I initially suspected it would be. The self test provided information which suggested I was split between visitor and resident in terms. In terms of my interactions with the web this didn’t suprise me too much as my web use is split between leisure and academic research and the former doesn’t require much time or skill investment.

What I did find particularly interesting however was to see from other student’s blogs not just how they where split in different ways between resident and visitor but also how they adopted different approaches to answering the question. For instance  Adam who’s blog I commented on engaged with the question by delving into the wider academic debates surrounding digital visitor and resident and what the competing ideas on the subject had been. Adam’s blog post differed from mine in this regard as I my post was more internally focused looking at my own self test in a little more detail and connecting my interest in the UOSM2008 module to my Web Science course. My perspective allowed mentor engage with the question by drawing a parralel to Web Science, however in doing so this made it very challenging to engage with the wider debate on how the idea of digital visitor and resident has been contested. This is in part due to working at a restricted word count.

Stefan elaborated on this point and allowed me to see picking this approach to answering the question as something which had both strengths and weaknesses as certain trade-off’s had to be made, for instance that I could have referenced Prensky. I have found this diversity of answer mirrored in how different students approached the question allowing me to see perspective s I otherwise wouldn’t.

Word Count: 300


Prensky, M. 2011. Digital Natives, Digital Immigrants. [Online] On the horizon (9)5.

My Comment response


Comment response to my blog:


Introductory Topic: Digital Visitors & Residents – UOSM2008

I chose this module as I am studying Web Science on the social science pathway, and I was interested in the intersection between this particular module and my degree. Web science explores the socio-technical aspects of the web such as the social shaping of technology. That is to say how the development of technology is a two way process. Whereby people are both affected by and affect how technologies such as the web are transformed through the way in which they are used (MacKenzie and Wajcman, 2011). With regards to this course I am interesting in exploring concepts such as digital visitors and residents to therefore gain a better understanding of the impact web technologies have on people’s online behaviors and vice versa. White, D. S., & Cornu, A. L. (2011) discussed this idea of a “paradigm shift” whereby newer web technologies in the form of social media such as Facebook and Twitter have changed how people use the web.

JISC mapping tool.png
JISC Mapping Tool – Image courtesy of David White – Learning & Technology researcher, University of Oxford

Drawing upon the results of the JISC mapping tool (left), I found myself to be split between digital resident and visitor. For instance most of my social media and community engagement is personal rather than institutional, but search engine and email usage is split between the two. Usage of Video sites such as YouTube are almost exclusively personal with any overlap with institutional being largely coincidental rather than intentional. What these findings mean for my digital literacy, and the impact that underlying web technologies have on the development of my personal learning network is something I hope to examine throughout this module.


Example visitor and resident maps. [online] JISC. 2014 Available at: [Accessed 11 Feb. 2018].

MacKenzie, D. and Wajcman, J. (2011). The social shaping of technology. Maidenhead [u.a.]: Open University Press.

White, D. S., & Cornu, A. L. (2011). Visitors and Residents: A new typology for online engagement. First Monday, 16(9).