Be visible or vanish

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After Cristina Rigutto’s informative seminar on post-publication digital engagement, we asked for her advice about blogging and how to increase our visibility online. Cristina reminded us that a key element of an academic’s profile is their digital footprint (including blogposts, Twitter feed, Instagram and webpages) – but to be effective in communicating your research online, you need people to find and follow you. We’ve all spent time trying to track people down online, sifting through a myriad of content – so how can you raise your profile to let people know you’re out there beavering away?

  • You need to be found on Google, the best way to do this is to create a Google Scholar profile. The profile can include all your output, not just peer reviewed content.
  • Put your presentations on Slideshare (one of the 10 most viewed sites in the world) it connects to Microsoft and LinkedIn.
  • Set up a YouTube channel in your name.
  • Wikipedia. – whilst Wikipedia is notoriously difficult to add content to you can easily insert a reference to your paper/ presentation into an existing page about your topic.
  • WordPress – put all the information about yourself in one place that then links out to your Twitter profile, Instagram account, blog etc.

It may not be practical to utilise all of these but any one will bump you up the list and help people connect with you.

Tips for academic blogging

BlogAn increasing number of academics are using blogs to reach a wider audience and share their research in a more comprehensible way. However, a staggering 81% of people will only read your first paragraph (71% the second, 63% the third and 32% the fourth, you get the idea if you’ve read this far…).

So the opening paragraph needs to contain your key message and words (detail can follow in subsequent paragraphs):

  • Keep to 300-750 words.
  • Repeat key words and their synonyms.
  • Use links inside the post including internal links to other posts.
  • Use lists as often as possible (see what we did there!) – a search engine reads html tags and will place your post higher on the results page.
  • Tweet a lot about the post – most people only catch a snapshot of the content on their twitter feeds, give your post a chance by shouting about it frequently!
  • Send as a Direct Message to anyone who may be interested – you don’t need to ask them to share it, you can just ask their opinion and often they will share your content anyway.

So there you have it, once you’ve set up your digital presence it is relatively easy and not too time consuming to maintain, build it into the everyday activities you carry out as an academic!

Jane Wooster and Kate Turton

 

Researching images on social media – nuts and bolts

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Images and videos are pervasive online, these days, web articles include at least one image or video. On Twitter, Facebook and Snapchat these visual contents are even more common, and social media platforms such as YouTube, Vimeo, Vine, Instagram, and Pinterest are entirely dedicated to their sharing.

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Images can emphasise textual messages, or even convey a message without text at all (Hankey et al., 2013), and they can increase the visibility of a tweet and how often it is shared (Yoon and Chung, 2013). There are so many images on social media that these platforms have become picture databases, and these have become subject to research. For example, Vis et al. (2013) explored images production and sharing practices on Twitter during the UK riots in 2011; Tiggemann and Zaccardo (2016) analysed Instagram images related to the #fitspiration movement, addressing their potential inspiration for viewers and negative effects on viewers’ body image; and Guidry et al. (2015) investigated the content and the engagement of pro- and anti-vaccine images shared on Pinterest.

My Ph.D. research uses one of these databases – it focuses on vaccine images used for advocacy that are shared on Twitter. Sourcing the images that are my data may sound simple, after all, I only need to download my data from Twitter, right? However, it is rather more complex than that. To start with, there are many different communities on Twitter, and they share images on a range of different topic. They may also share images on the same topic from different angles; for example, if we search #health on Twitter, we will see pictures related to healthy food, obesity, fitness, losing weight, public health policy, etc. So, the biggest challenges are how to find the communities of interest and then to develop a data analysis strategy that uncovers how they use their pictures.

To help me narrow the potential field of image research for my PhD, I asked the following questions:

  1. What topic am I interested in? Which communities do I want to study?
  2. Which social media outlets would I find most interesting/useful for my research?
  3. Each social media platform is used by different audiences, so it is important to think about the overall question we are asking. For example, young adults use Facebook, whereas teenagers prefer Snapchat, and Chinese people may be on Weibo.
  4. Where are these communities from? Which language(s) do they use?
  5. If we focus our research on Europe, we have to take into account that Europeans speak different languages. If we focus on English language, we have to consider that our images will come from all over the world, but especially from the US, UK and Australia.

Afterward this initial sifting, I had more questions to answer:

  1. What keywords should I use to search on my chosen social platform (in my case, Twitter)?
  2. Each topic and each community has its own “slang” or “dialect” and therefore keywords. On Twitter, for example, users in favour of vaccinations tweet their content including the hashtag #vaccineswork, whereas people against vaccines use mainly the hashtag #vaxxed and/or #CDCwhistleblower.
  3. How can I find the relevant keywords?
  4. Previous research on social media can suggest some terms; in my case, keywords such as vaccine(s), vaccination(s), vaccinate(d) and immunes(z)ation (Love et al., 2013; Salathé et al., 2013). Searching for these generic words, I found both tweets with and without hashtags that talked about vaccines. However, some communities use specific keywords which may not include these terms (e.g. #vaxxed) and they may use these keywords to label their tweets as relevant to the topic. For example, a tweet claiming “They’re poisoning our children #CDCwhislteblower” and showing an image with a child whilst being vaccinated, would be relevant to vaccinations even if it did not mention “vaccine” or “vaccination”. This tweet would not appear in my research if I set my data collection using only generic words, thus I needed to search for relevant hashtags as well.
  5. How do I find relevant hashtags?
  6. A first step would be considering which hashtags previous studies used, then searching Twitter for generic hashtags and see which other hashtags people use. There are also some online tools that can be helpful, such as Hashtagify.me, Get Tags and RiteTag.com. These online software packages suggest correlated hashtags and their popularity.

Answering these questions helps us define the criteria for data collection, but they also show how complicated research on images shared on social media is. As with any data collection method, planning, defining and developing are key for research drawing on online images. We need to be able to justify the approach we took and show that the data collection process is robust. This means, as with many other types of data collection, that we need to pilot and test our data collection methods ensuring that they deliver the material we anticipate and which will validly help us to address our research question. There are so many pictures online, uploaded, downloaded, edited and shared, that the choice of image collection methods becomes key to ensuring the quality of the study overall.

 

Elena Milani

 

References

Hankey, S., Longley, T., Tuszynski, M. and Indira Ganesh, M. (2013). Visualizing Information for Advocacy. Nederlands: Tactical Technology Collective.

Love, B., Himelboim, I., Holton, A. and Stewart, K. (2013) Twitter as a source of vaccination information: content drivers and what they are saying. American Journal of Infection Control [online]. 41(6), pp. 568-570.

Guidry, J.P., Carlyle, K., Messner, M. and Jin, Y. (2015) On pins and needles: How vaccines are portrayed on Pinterest. Vaccine [online]. 33(39), pp. 5051-5056.

Salathé, M., Vu, D.Q., Khandelwal, S. and Hunter, D.R. (2013) The dynamics of health behavior sentiments on a large online social network. EPJ Data Science [online]. 2(1), pp. 1-12.

Tiggemann, M. and Zaccardo, M. (2016) ‘Strong is the new skinny’: A content analysis of #fitspiration images on Instagram. Journal of Health Psychology [online].

Vis, F., Faulkner, S., Parry, K., Manyukhina, Y. and Evans, L. (2013) Twitpic-ing the riots: analysing images shared on Twitter during the 2011 UK riots. In: Weller, K., Bruns, A., Burgess, J., Mahrt, M. and Puschmann, C. (2013) Twitter and Society. New York: Peter Lang Publishing Inc., pp. 385-398.

Yoon, J. and Chung, E. (2013) How images are conversed on twitter? Proceedings of the American Society for Information Science and Technology [online]. 50(1), pp. 1-5.