Tweet or perish – practical tips on post-publication digital engagement

Last month, Cristina Rigutto (communications consultant and social media editor at Public Understanding of Science Journal) came to speak to us, as part of our ongoing series of science communication seminars, about increasing the impact of our research through ‘post-publication digital engagement’. This isn’t about creating ‘impact’ in the REF sense of the word but the use of social media and online news streams to engage a wider public audience with academic research. Here are Cristina’s top practical, time-saving tips for using digital tools in science communication.

Twitter for conferences

Using Twitter is a great way to engage people with your research, especially when you’re attending or presenting at meetings and conferences. Before the conference starts, check Twitter to find out who else will be attending and get in touch with people whose research interests match yours. Let them know that you’ll be presenting and post a visual abstract of your talk or poster to whet their appetite. If you’re going to cite someone else’s work in your presentation, let them know and offer to send them a copy of your slides after your talk. When you get to the conference, tweet your session number and the time and location of your talk along with a picture of your first slide – you’ll encourage more people to come along! Don’t forget to prepare a couple of tweets in advance and ask a friend/colleague to tweet them to share the most salient points from your presentation as you speak. And finally, tweet a link to your slides so that people can download or read them after the event.

Before your presentation:

• Find the speaker and attendee list, network before the event.
• Create visuals to appeal to lay public/ journalists.
• Encourage people to ask questions about your work (in more than one language si possible).
• If you’re citing someone, let them know, offer to send them a copy of your presentation, everyone likes a freebie!
• Tweet session number, time, room number.
• Prepare tweets in advance.

Live tweet  – now an essential part the conference experience!

After your presentation:

• Be open and willing to continue the conversation after the conference has ended.
• Tweet a download of your presentation.

Twitter is also useful for networking outside conference settings. If you publish a paper, tweet the DOI link, include a screenshot of the abstract and use appropriate hashtags. Use direct messages to contact people who may be interested in your work – share your paper and ask them for their opinion. Search for people by discipline or profession to find academics, journalists, students and bloggers who might be interested in your research. Follow them and they might follow you back! You can also use altmetrics to find out who is liking or retweeting your research and then you can get in touch with them.

• Tweet your paper using the journal’s DOI link (ensures google will pick it up), use appropriate hashtags, include a screen shot of the abstract.
• Direct message (DM) people who may be interested in your work – ask their opinion.
• Include images/ infographics/ sketchnotes.
• Find relevant academics, science journalists and bloggers and contact with links to a short version e.g. blogpost rather than original paper.
• Use Journal altmetrics to find out who has tweeted your research – record the names and invite to suitable event.
• Tweet according to a country’s local time.
• The right followers is more important than the number.

Instagram

For many people Instagram seems to be mainly about celebrity lifestyle photos, holidays, culinary creations and well, a sepia tinted view on life – but there is definitely a use for it in the academic world:

It is very difficult to predict your Instagram audience in comparison to Twitter; however, you can often reach a larger lay public. Take the student who spent his summer holidays collecting faecal images (yes, taking pictures of poo!). After posting them on Instagram he ended up answering questions from the general public about the excreta related science – people were fascinated – and this is a good point, people are naturally curious so show them something interesting.

So, how can you use Instagram to show the human side of your research?

• Use it during the research process.
• Use a wide range of pictures – the inside of a lab is fascinating if you’ve never seen one before, try SciArt and selfies!
• Introduce staff members.
• Always include accurate and complete information about the image in the text.
• Include some humour
• Post some videos (more popular than images) – these don’t have to be long or artfully directed, just a few seconds of you talking works!

In short, we came away with a lot of practical tips and ideas for promoting our research that won’t take up too much time. See you all in the social media arena!

Jane Wooster and Kate Turton

Researching images on social media – nuts and bolts

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.

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.