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Enquiry Article

Social bear on in social media: A new method to evaluate the social affect of research

• Cristina M. Pulido,
• Gisela Redondo-Sama,
• Teresa Sordé-Martí,
• Ramon Flecha

x

• Published: August 29, 2018
• https://doi.org/10.1371/journal.pone.0203117

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Abstruse

The social impact of inquiry has usually been analysed through the scientific outcomes produced nether the auspices of the inquiry. The growth of scholarly content in social media and the apply of altmetrics by researchers to track their work facilitate the advancement in evaluating the impact of research. However, there is a gap in the identification of show of the social impact in terms of what citizens are sharing on their social media platforms. This article applies a social impact in social media methodology (SISM) to identify quantitative and qualitative evidence of the potential or real social impact of enquiry shared on social media, specifically on Twitter and Facebook. Nosotros ascertain the social affect coverage ratio (SICOR) to identify the pct of tweets and Facebook posts providing data about potential or actual social impact in relation to the full corporeality of social media data institute related to specific research projects. We selected ten projects in different fields of knowledge to summate the SICOR, and the results point that 0.43% of the tweets and Facebook posts collected provide linkages with data nigh social bear on. Withal, our assay indicates that some projects accept a high percentage (iv.98%) and others take no evidence of social touch on shared in social media. Examples of quantitative and qualitative evidence of social bear on are provided to illustrate these results. A general finding is that novel evidences of social impact of research tin can exist institute in social media, becoming relevant platforms for scientists to spread quantitative and qualitative evidence of social touch on in social media to capture the interest of citizens. Thus, social media users are showed to be intermediaries making visible and assessing evidence of social impact.

Citation: Pulido CM, Redondo-Sama Chiliad, Sordé-Martí T, Flecha R (2018) Social impact in social media: A new method to evaluate the social touch of research. PLoS One thirteen(eight): e0203117. https://doi.org/10.1371/journal.pone.0203117

Editor: Sergi Lozano, Institut Català de Paleoecologia Humana i Evolució Social (IPHES), Espana

Received: November 8, 2017; Accustomed: August 15, 2018; Published: August 29, 2018

Copyright: © 2018 Pulido et al. This is an open up access article distributed nether the terms of the Artistic Commons Attribution License, which permits unrestricted use, distribution, and reproduction in whatsoever medium, provided the original author and source are credited.

Information Availability: All relevant data are within the paper and its Supporting Information files.

Funding: The inquiry leading to these results has received funding from the seventh Framework Programme of the European Committee under the Grant Agreement n° 613202 P.I. Ramon Flecha, https://ec.europa.european union/inquiry/fp7/index_en.cfm. The funders had no function in study pattern, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests be.

Introduction

The social touch on of research is at the core of some of the debates influencing how scientists develop their studies and how useful results for citizens and societies may be obtained. Concrete strategies to accomplish social touch in item research projects are related to a broader understanding of the role of science in gimmicky society. In that location is a demand to explore dialogues between science and society not simply to communicate and disseminate science just too to achieve social improvements generated past science. Thus, the social bear on of research emerges as an increasing concern within the scientific community [1]. As Bornmann [2] said, the cess of this type of impact is badly needed and is more difficult than the measurement of scientific affect; for this reason, it is urgent to advance in the methodologies and approaches to measuring the social bear upon of inquiry.

Several authors take approached the conceptualization of social impact, observing a lack of generally accepted conceptual and instrumental frameworks [3]. It is common to detect a wide range of topics included in the contributions nearly social affect. In their analysis of the policies affecting country apply, Hemling et al. [four] considered various domains in social impact, for instance, agronomical employment or health risk. Moving to the field of flora and animal, Wilder and Walpole [5] studied the social impact of conservation projects, focusing on qualitative stories that provided information about changes in attitudes, behaviour, wellbeing and livelihoods. In an all-encompassing report by Godin and Dore [six], the authors provided an overview and framework for the cess of the contribution of science to gild. They identified indicators of the impact of scientific discipline, mentioning some of the most relevant weaknesses and developing a typology of impact that includes eleven dimensions, with ane of them being the impact on guild. The subdimensions of the affect of science on lodge focus on individuals (wellbeing and quality of life, social implication and practices) and organizations (speeches, interventions and actions). For the authors, social impact "refers to the bear on knowledge has on welfare, and on the behaviours, practices and activities of people and groups" (p. 7).

In improver, the terms "social impact" and "societal impact" are sometimes used interchangeably. For example, Bornmann [2] said that due to the difficulty of distinguishing social benefits from the superior term of societal benefits, "in much literature the term 'social impact' is used instead of 'societal impact'"(p. 218). However, in other cases, the distinction is fabricated [3], as in the nowadays inquiry. Similar to the definition used by the European Committee [7], social bear upon is used to refer to economic touch on, societal impact, environmental impact and, additionally, homo rights bear upon. Therefore, we use the term social impact every bit the broader concept that includes social improvements in all the in a higher place mentioned areas obtained from the transference of research results and representing positive steps towards the fulfilment of those officially defined social goals, including the UN Sustainable Development Goals, the European union 2020 Agenda, or similar official targets. For instance, the Europe 2020 strategy defines v priority targets with concrete indicators (employment, research and development, climate change and free energy, pedagogy and poverty and social exclusion) [8], and we consider the targets addressed by objectives defined in the specific call that funds the inquiry projection.

This understanding of the social impact of enquiry is continued to the cosmos of the Social Impact Open up Repository (SIOR), which constitutes the first open up repository worldwide that displays, cites and stores the social impact of enquiry results [9]. The SIOR has linked to ORCID and Wikipedia to allow the synergies of spreading data about the social affect of research through diverse channels and audiences. It is relevant to mention that currently, SIOR includes evidence of real social touch on, which implies that the research results have led to actual improvements in society. Nonetheless, it is common to notice evidence of potential social affect in research projects. The potential social impact implies that in the development of the research, there has been some evidence of the effectiveness of the research results in terms of social bear upon, but the results have non yet been transferred.

Additionally, a common confusion is institute amidst the uses of broadcasting, transference (policy bear upon) and social impact. While broadcasting means to disseminate the knowledge created by research to citizens, companies and institutions, transference refers to the use of this knowledge by these dissimilar actors (or others), and finally, as already mentioned, social touch refers to the actual improvements resulting from the use of this knowledge in relation to the goals motivating the research projection (such as the United Nations Sustainable Evolution Goals). In the present research [3], it is argued that "social impact can be understood as the culmination of the prior three stages of the research" (p.iii). Therefore, this study builds on previous contributions measuring the dissemination and transference of research and goes beyond to propose a novel methodological arroyo to rails social touch on evidences.

In fact, the contribution that we develop in this article is based on the creation of a new method to evaluate the bear witness of social impact shared in social media. The evaluation proposed is to measure the social impact coverage ratio (SICOR), focusing on the presence of evidence of social impact shared in social media. Then, the article beginning presents some of the contributions from the literature review focused on the research on social media every bit a source for obtaining primal information for monitoring or evaluating unlike inquiry purposes. 2nd, the SISM (social impact through social media) methodology[10] developed is introduced in item. This methodology identifies quantitative and qualitative evidence of the social impact of the research shared on social media, specifically on Twitter and Facebook, and defines the SICOR, the social impact coverage ratio. Next, the results are discussed, and lastly, the main conclusions and further steps are presented.

Literature review

Social media research includes the assay of citizens' voices on a broad range of topics [11]. According to quantitative data from April 2017 published by Statista [12], Twitter and Facebook are included in the top ten leading social networks worldwide, as ranked by the number of active users. Facebook is at the superlative of the list, with 1,968 million active users, and Twitter ranks x^thursday, with 319 1000000 agile users. Between them are the following social networks: WhatsApp, YouTube, Facebook Messenger, WeChat, QQ, Instagram,Qzone and Tumblr. If we expect at altmetrics, the tracking of social networks for mentions of inquiry outputs includes Facebook, Twitter, Google+,LinkedIn, Sina Weibo and Pinterest. The social networks mutual to both sources are Facebook and Twitter. These are also pop platforms that take a relevant coverage of scientific content and easy access to data, and therefore, the inquiry projects selected here for application of the SISM methodology were chosen on these platforms.

Chew and Eysenbach [13] studied the presence of selected keywords in Twitter related to public health problems, particularly during the 2009 H1N1 pandemic, identifying the potential for health regime to use social media to respond to the concerns and needs of society. Crooks et al.[fourteen] investigated Twitter activity in the context of a 5.8 magnitude earthquake in 2011 on the East Coast of the United States, concluding that social media content can be useful for event monitoring and tin can complement other sources of information to improve the understanding of people's responses to such events. Conversations among young Canadians posted on Facebook and analysed by Martinello and Donelle [15] revealed housing and transportation every bit master environmental concerns, and the project FoodRisc examined the role of social media to illustrate consumers' quick responses during nutrient crisis situations [sixteen]. These types of contributions illustrate that social media research implies the agreement of citizens' concerns in different fields, including in relation to science.

Research on the synergies between scientific discipline and citizens has increased over the years, co-ordinate to Fresco [17], and there is a growing interest among researchers and funding agencies in how to facilitate communication channels to spread scientific results. For instance, in 1998, Lubchenco [18] advocated for a social contract that "represents a delivery on the part of all scientists to devote their energies and talents to the almost pressing problems of the 24-hour interval, in proportion to their importance, in exchange for public funding"(p.491).

In this framework, the recent debates on how to increase the affect of research have acquired relevance in all fields of noesis, and major developments address the methods for measuring information technology. As highlighted past Feng Xia et al. [19], social media establish an emerging approach to evaluating the impact of scholarly publications, and it is relevant to consider the influence of the periodical, subject, publication year and user type. The authors revealed that people'south concerns differ past subject and observed more interest in papers related to everyday life, biology, and earth and ecology sciences. In the field of biomedical sciences, Haustein et al. [20] analysed the dissemination of periodical manufactures on Twitter to explore the correlations between tweets and citations and proposed a framework to evaluate social media-based metrics. In fact, different studies accost the relationship between the presence of articles on social networks and citations [21]. Bornmann [22] conducted a example study using a sample of one,082 PLOS journal articles recommended in F1000 to explore the usefulness of altmetrics for measuring the broader bear upon of research. The author presents testify about Facebook and Twitter as social networks that may indicate which papers in the biomedical sciences can be of interest to broader audiences, not just to specialists in the area. One aspect of particular interest resulting from this contribution is the potential to utilise altmetrics to measure the broader impacts of research, including the societal touch. Nevertheless, most of the studies investigating social or societal impact lack a conceptualization underlying its measurement.

To the best of our knowledge, the assessment of social impact in social media (SISM) has developed according to this gap. At the cadre of this report, we present and discuss the results obtained through the awarding of the SICOR (social touch coverage ratio) with examples of evidence of social impact shared in social media, particularly on Twitter and Facebook, and the implications for further enquiry.

Following these previous contributions, our research questions were as follows: Is there evidence of social impact of research shared by citizens in social media? If so, is in that location quantitative or qualitative evidence? How can social media contribute to identifying the social impact of enquiry?

Methods and data presentation

A group of new methodologies related to the analysis of online data has recently emerged. One of these emerging methodologies is social media analytics [23], which was initially used virtually in the marketing inquiry field just as well came to exist used in other domains due to the multiple possibilities opened up by the availability and richness of the data for different inquiry purposes. Likewise, the business organization of how to evaluate the social bear on of research as well as the evolution of methodologies for addressing this concern has occupied primal attending. The development of SISM (Social Impact in Social Media) and the application of the SICOR (Social Impact Coverage Ratio) is a contribution to advocacy in the evaluation of the social touch of research through the analysis of the social media selected (in this case, Twitter and Facebook). Thus, SISM is novel in both social media analytics and amid the methodologies used to evaluate the social impact of enquiry. This development has been made under Touch on-EV, a inquiry project funded nether the Framework Programme FP7 of the Advisers-General for Research and Innovation of the European Committee. The primary difference from other methodologies for measuring the social bear on of research is the disentanglement between dissemination and social bear upon. While altmetrics is aimed at measuring research results disseminated beyond academic and specialized spheres, SISM contribute to advancing this measurement by shedding calorie-free on to what extent evidence of the social impact of research is constitute in social media data. This involves the need to differentiate between tweets or Facebook posts (Fb/posts) used to disseminate research findings from those used to share the social impact of inquiry. We focus on the latter, investigating whether there is evidence of social impact, including both potential and existent social bear on. In fact, the question is whether enquiry contributes and/or has the potential to contribute to meliorate the society or living conditions because one of these goals defined. What is the bear witness? Next, nosotros detail the application of the methodology.

Data collection

To develop this report, the starting time stride was to select enquiry projects with social media information to exist analysed. The selection of research projects for application of the SISM methodology was performed according to 3 criteria.

Criteria 1. Pick of success projects in FP7. The projects were success stories of the seven^th Framework Programme (FP7) highlighted by the European Commission [24] in the fields of noesis of medicine, public health, biological science and genomics. The FP7 published calls for project proposals from 2007 to 2013. This implies that most of the projects funded in the final period of the FP7 (2012 and 2013) are finalized or in the concluding phase of implementation.

Criteria 2. Menstruum of implementation. We selected projects in the 2012–2013 period considering they combine recent research results with higher possibilities of having Twitter and Facebook accounts compared with projects of previous years, every bit the presence of social accounts in research increased over this period.

Criteria 3. Twitter and Facebook accounts. It was crucial that the selected projects had active Twitter and Facebook accounts.

Table i summarizes the criteria and the final number of projects identified. As shown, x projects met the defined criteria. Projects in medical research and public health had higher presence.

Later the selection of projects, we defined the timeframe of social media data extraction on Twitter and Facebook from the starting date of the project until the day of the search, equally presented in Table 2.

The 2nd step was to define the search strategies for extracting social media data related to the enquiry projects selected. In this line, nosotros defined three search strategies.

Strategy 1. To extract messages published on the Twitter account and the Facebook folio of the selected projects. We listed the Twitter accounts and Facebook pages related to each project in guild to look at the available data. In this example, it is important to clarify that the tweets published under the corresponding Twitter projection business relationship are original tweets or retweets fabricated from this business relationship. It is relevant to mention that in one case, the Twitter account and Facebook page were linked to the website of the research grouping leading the project. In this case, we selected tweets and Facebook posts related to the project. For instance, in the case of the Twitter account, the research grouping created a specific hashtag to publish letters related to the project; therefore, we selected but the tweets published under this hashtag. In the analysis, we prioritized the analysis of the tweets and Facebook posts that received some type of interaction (likes, retweets or shares) considering such interaction is a proxy for citizens' involvement. In doing so, we used the R program and NVivoto extract the data and proceed with the assay. Once we obtained the information from Twitter and Facebook, nosotros were able to have an overview of the data to be further analysed, as shown in Table 3.

We focused the second and 3rd strategies on Twitter data. In both strategies, we extracted Twitter data direct from the Twitter Advanced Search tool, as the API connected to NVivo and the R program covers only a specific menstruum of time express to seven/9 days. Therefore, the use of the Twitter Advanced Search tool made it possible to obtain celebrated information without a menstruation limitation. We downloaded the results in PDF and so uploaded them to NVivo.

Strategy 2. To use the project acronym combined with other keywords, such as FP7 or EU. This strategy made it possible to obtain tweets mentioning the projection. Table 4 presents the number of tweets obtained with this strategy.

Strategy iii. To utilise searchable inquiry results of projects to obtain Twitter data. We defined a list of research results, one for each project, and converted them into keywords. We selected one searchable keyword for each project from its website or other relevant sources, for instance, the brief presentations prepared by the European Commission and published in CORDIS. Once nosotros had the searchable research results, we used the Twitter Advanced Search tool to obtain tweets, as presented in Tabular array 5.

The sum of the data obtained from these three strategies allowed united states to obtain a total of iii,425 tweets and 1,925 posts on public Facebook pages. Table 6 presents a summary of the results.

We imported the data obtained from the three search strategies into NVivo to analyse. Next, nosotros select tweets and Facebook posts providing linkages with quantitative or qualitative evidence of social bear on, and nosotros complied with the terms of service for the social media from which the information were collected. Past quantitative and qualitative testify, we hateful data or information that shows how the implementation of research results has led to improvements towards the fulfilment of the objectives defined in the EU2020 strategy of the European Commission or other official targets. For instance, in the example of quantitative evidence, we searched tweets and Facebook posts providing linkages with quantitative information most improvements obtained through the implementation of the research results of the project. In relation to qualitative bear witness, for instance, we searched for testimonies that show a positive evaluation of the improvement due to the implementation of research results. In relation to this stride, information technology is important to highlight that social media users are intermediaries making visible bear witness of social impact. Users oft share show, sometimes sharing a link to an external resource (e.chiliad., a video, an official study, a scientific article, news published on media). We identified testify of social touch in these sources.

Data analysis

Nosotros analysed all tweets and Facebook posts collected (3,425 tweets and ane,925 Facebook posts) to calculate the ratio of social media information with show of social impact in relation to the full amount of social media data extracted from the research projects selected. The aim was to answer the question whether or not in that location is evidence of social bear upon shared by citizens in social media. In one case we had the tweets and Facebook posts selected for each project, we identified the number of tweets and Facebook posts responding or not to the criteria of presenting bear witness of the social impact of inquiry. In the last step of this search, we defined a ratio of coverage adjusted to this adding called the SICOR, the social bear upon coverage ratio: where:

γ_i is the total number of messages obtained about project i with prove of social impact on social media platforms (Twitter, Facebook, Instagram, etc.);

T _i is the total number of messages from project ion social media platforms (Twitter, Facebook, Instagram, etc.); and

northward is the number of projects selected.

The result is expressed in percentages. In this paper, we use the SICOR for Twitter and Facebook thus: and

Analytical categories and codebook

The researchers who carried out the analysis of the social media data collected are specialists in the social impact of research and research on social media. Before conducting the total analysis, two aspects were guaranteed. Outset, how to identify evidence of social impact relating to the targets divers by the EU2020 strategy or to specific goals defined past the call addressed was clarified. Second, nosotros held a pilot to test the methodology with one research project that nosotros know has led to considerable social impact, which allowed us to clarify whether or non it was possible to detect evidence of social touch shared in social media. One time the pilot showed positive results, the next step was to extend the analysis to another set of projects and finally to the whole sample. The construction of the belittling categories was defined a priori, revised appropriately and lastly applied to the total sample.

Different observations should exist made. Beginning, in this previous analysis, nosotros constitute that the tweets and Facebook users play a key role as "intermediaries," serving as bridges between the larger public and the prove of social impact. Social media users usually share a quote or paragraph introducing show of social impact and/or link to an external resources, for case, a video, official study, scientific commodity, news story published on media, etc., where testify of the social impact is available. This fact has implications for our study, every bit our unit of analysis is all the data included in the tweets or Facebook posts. This means that our analysis reaches the external resource linked to find evidence of social impact, and for this reason, we defined tweets or Facebook posts providing linkages with information about social impact.

2nd, the other important aspect is the analysis of the users' contour descriptions, which requires much more development in future enquiry given the existing limitations. For example, some profiles are users' restricted due to privacy reasons, so the information is non available; other accounts accept just the proper noun of the user with no clarification of their profile available. Therefore, nosotros gave priority to the identification of evidence of social impact including whether a post obtained interaction (retweets, likes or shares) or was published on accounts other than that of the research projection itself. In the case of the profile analysis, we added only an exploratory preliminary result considering this requires further development. Because all these previous details, the codebook (see Table vii) that we nowadays equally follows is a result of this previous research.

How to analyse Twitter and Facebook information

To illustrate how we analysed data from Twitter and Facebook, nosotros provide i instance of each type of evidence of social bear upon defined, considering both real and potential social impact, with the type of interaction obtained and the profiles of those who accept interacted.

QUANESISM. Tweet past ZeroHunger Claiming @ZeroHunger published on iii May 2016. Text: How re-using nutrient waste product for fauna feed cuts carbon emissions.-NOSHAN project hubs.ly/H02SmrP0. vii retweets and v likes.

The unit of measurement of analysis is all the content of the tweet, including the external link. If nosotros limited our assay to the tweet itself, it would not be testify. Examining the external link is necessary to find whether at that place is bear witness of social bear on. The aim of this project was to investigate the process and technologies needed to employ food waste for feed production at low cost, with low energy consumption and with a maximal evaluation of the starting wastes. This tweet provides a link to news published in the PHYS.org portal [25], which specializes in science news. The news story includes an interview with the master researcher that provides the post-obit quotation with quantitative evidence:

'Our results demonstrated that with a NOSHAN 10 percentage mix diet, for every kilogram of broiler chicken feed, carbon dioxide emissions were reduced by 0.3 kg compared to a non-food waste product diet,' explains Montse Jorba, NOSHAN project coordinator. 'If i percent of total chicken broiler feed in Europe was switched to the 10 percent NOSHAN mix diet, the total amount of CO2 emissions avoided would be 0.62 1000000 tons each year.'[25]

This quantitative testify "a NOSHAN 10 pct mix diet, for every kilogram of broiler chicken feed, carbon dioxide emissions carbon dioxide emissions were reduced by 0.three kg to a non-nutrient waste matter diet" is linked straight with the Europe 2020 target of Climate Change & Energy, specifically with the target of reducing greenhouse gas emissions by 20% compared to the levels in 1990 [8]. The illustrative extrapolation the coordinator mentioned in the news is too an example of quantitative evidence, although is an extrapolation based on the specific inquiry result.

This tweet was captured by the Acronym search strategy. It is a bulletin tweeted past an account that is not related to the research projection. The twitter account is that of the Zero Hunger Claiming motility, which supports the goals of the UN. The interaction obtained is 7 retweets and 5 likes. Regarding the profiles of those who retweeted and clicked "like", at that place were activists, a announcer, an eco-friendly citizen, a global news service, restricted profiles (no information is bachelor on those who have retweeted) and one account with no information in its contour.

The following case illustrates the analysis of QUALESISM: Tweet by @eurofitFP7 published on4 October 2016. Text: See our great new EuroFIT video on youtube! https://t.co/TocQwMiW3c ix retweets and 5 likes.

The aim of this project is to improve health through the implementation of ii novel technologies to achieve a healthier lifestyle. The tweet provides a link to a video on YouTube on the project's results. In this video, we found qualitative evidence from people who tested the EuroFit programme; there are quotes from men who said that they have experienced improved wellness results using this method and that they are more aware of how to manage their wellness:

One finish-user said: I have really astonishing results from the showtime, considering I managed to change a lot of things in my life. And other one: I was more witting of what I ate, I was more than witting of taking more than steps throughout the mean solar day and also continuing up a little more. [26]

The research applies the well researched scientific evidence to the management of health issues in daily life. The video presents the inquiry but likewise includes a section where terminate-users talk about the wellness improvements they experienced. The quotes extracted are some examples of the testimonies nerveless. All concur that they accept improved their health and learned salubrious habits for their daily lives. These are examples of qualitative prove linked with the target of the telephone call Health.2013.3.three–one—Social innovation for health promotion [27] that has the objectives of reducing sedentary habits in the population and promoting good for you habits. This research contributes to this target, as we see in the video testimonies. Regarding the interaction obtained, this tweet accomplished 9 retweets and v likes. In this case, the profiles of the interacting citizens show involvement in sport issues, including sport trainers, sport enthusiasts and some researchers.

To summarize the assay, in Table 8 beneath, we provide a summary with examples illustrating the evidence constitute.

Quantitative bear witness of social touch on in social media

There is a greater presence of tweets/Fb posts with quantitative prove (14) than with qualitative evidence (ix) in the total number of tweets/Fb posts identified with evidence of social impact. Almost of the tweets/Fb posts with quantitative show of social touch on are from scientific articles published in peer-reviewed international journals and show potential social impact. In Tabular array 8, nosotros innovate 3 examples of this type of tweets/Fb posts with quantitative bear witness:

The commencement tweet with quantitative social touch selected is from project 7. The aim of this project was to provide loftier-quality scientific prove for preventing vitamin D deficiency in European citizens. The tweet highlighted the chief contribution of the published report, that is, "Weekly consumption of vii vitamin D-enhanced eggs has an of import impact on wintertime vitamin D status in adults" [28]. The quantitative evidence shared in social media was extracted from a news publication in a web log on health news. This web log collects scientific manufactures of enquiry results. In this case, the blog disseminated the inquiry event focused on how vitamin D-enhanced eggs improve vitamin D deficiency in wintertime, with the published results obtained by the research team of the project selected. The quantitative evidence illustrates that the group of adults who consumed vitamin D-enhanced eggs did not endure from vitamin D deficiency, equally opposed to the control grouping, which showed a pregnant decrease in vitamin D over the winter. The specific testify is the following extracted from the commodity [28]:

With the use of a within-group analysis, information technology was shown that, although serum 25(OH) D in the control group significantly decreased over wintertime (mean ± SD: -6.4 ± half dozen.7 nmol/L; P = 0.001), there was no modify in the two groups who consumed vitamin D-enhanced eggs (P>0.1 for both. (p. 629)

This evidence contributes to achievement of the target defined in the call addressed that is KBBE.2013.2.2–03—Food-based solutions for the eradication of vitamin D deficiency and health promotion throughout the life bicycle [29]. The quantitative evidence shows how the consumption of vitamin D-enhanced eggs reduces vitamin D deficiency.

The second example of this table corresponds to the case of quantitative show of social touch provided in the previous section.

The third example is a Facebook postal service from projection 3 that is likewise tweeted. Therefore, this evidence was published in both social media sources analysed. The aim of this project was to measure a range of chemical and physical environmental hazards in food, consumer products, water, air, racket, and the congenital environment in the pre- and postnatal early on-life periods. This Facebook post and tweet links straight to a scientific article [30] that shows the precision of the spectroscopic platform:

Using 1H NMR spectroscopy we characterized brusque-term variability in urinary metabolites measured from 20 children aged eight–9 years former. Daily spot morning, night-fourth dimension and pooled (50:fifty morning and dark-time) urine samples beyond vi days (18 samples per child) were analysed, and 44 metabolites quantified. Intraclass correlation coefficients (ICC) and mixed effect models were applied to assess the reproducibility and biological variance of metabolic phenotypes. Excellent analytical reproducibility and precision was demonstrated for the 1H NMR spectroscopic platform (median CV 7.2%)^.(p.1)

This evidence is linked to the target defined in the call "ENV.2012.6.iv–three—Integrating environmental and health data to advance noesis of the role of environs in human health and well-beingness in support of a European exposome initiative" [31]. The evidence provided shows how the project's results have contributed to building technology for improving the data drove to advance in the knowledge of the role of the environment in human health, especially in early life. The interaction obtained is one retweet from a citizen from Nigeria interested in health bug, co-ordinate to the information available in his profile.

Qualitative show of social impact in social media

We found qualitative prove of the social impact of different projects, every bit shown in Table 9. Similarly to the quantitative evidence, the qualitative cases also demonstrate potential social bear on. The iii examples provided have in common that they are tweets or Facebook posts that link to videos where the end users of the research projection explain their improvements once they have implemented the research results.

The first tweet with qualitative evidence selected is from project four. The aim of this projection is to produce a system that helps in the prevention of obesity and eating disorders, targeting young people and adults [32]. The twitter account that published this tweet is that of the Future and Emerging Technologies Programme of the European Committee, and a link to a Euronews video is provided. This video shows how the patients using the technology developed in the enquiry achieved control of their eating disorders, through the testimonies of patients commenting on the positive results they have obtained. These testimonies are included in the news commodity that complements the video. An instance of these testimonies is as follows:

Pierre Vial has lost 43 kilos over the past nine and a half months. He and other patients at the eating disorder clinic explain the furnishings obesity and anorexia take had on their lives. Another patient, Karin Borell, however has some months to become at the dispensary but, later on decades of battling anorexia, is first to exist able to visualise life without the disease: "On a adept mean solar day I come across myself living a normal life without an eating disorder, without issues with food. That's really all I wish right now".[32]

This qualitative evidence shows how the research results contribute to the achievement of the target goals of the telephone call addressed:"ICT-2013.five.1—Personalised wellness, agile ageing, and contained living". [33] In this instance, the results are robust, particularly for people suffering chronic diseases and desiring to improve their wellness; people who take applied the research findings are improving their eating disorders and better managing their health. The value of this prove is the inclusion of the patients' voices stating the impact of the enquiry results on their health.

The second example is a Facebook post from project 9, which provides a link to a Euronews video. The aim of this projection is to bring some tools from the lab to the farm in club to guarantee a better management of the farm and beast welfare. In this video [34], at that place are quotes from farmers using the new organization developed through the research results of the projection. These quotes show how use of the new system is improving the management of the subcontract and the wellness of the animals; some examples are provided:

Cameras and microphones aid me detect in real time when the animals are stressed for whatever reason," explained farmer Twan Colberts. "And so I tin can find solutions faster and in more efficient ways, without me beingness constantly here, checking each animal."

This show shows how the inquiry results contribute to addressing the objectives specified in the call "KBBE.2012.1.1–02—Creature and farm-axial approach to precision livestock farming in Europe" [29], particularly, to improve the precision of livestock farming in Europe. The interaction obtained is composed of6 likes and ane share. The profiles are diverse, but some of them do not disclose personal information; others have not added a contour description, and only their name and photo are available.

Interrater reliability (kappa)

The analysis of tweets and Facebook posts providing linkages with information nearly social impact was conducted post-obit a content analysis method in which reliability was based on a peer review process. This sample is composed of 3,425 tweets and 1,925 Fb/posts. Each tweet and Facebook post was analysed to identify whether or not it contains testify of social impact. Each researcher has the codebook a priori. Nosotros used interrater reliability in examining the agreement between the two raters on the assignment of the categories divers through Cohen's kappa. Nosotros used SPSS to calculate this coefficient. Nosotros exported an excel sail with the sample coded by the ii researchers existence 1 (is evidence of social touch, either potential or real) and 0 (is not testify of social affect) to SPSS. The cases where agreement was not achieved were not considered equally containing evidence of social impact. The result obtained is 0.979; considering the interpretation of this number co-ordinate to Landis & Koch [35], our level of agreement is nigh perfect, and thus, our analysis is reliable. To sum upwardly the data analysis, the description of the steps followed is explained:

Step 1. Data analysis I. We included all data collected in an excel sheet to proceed with the analysis. Prior to the assay, researchers read the codebook to keep in mind the information that should be identified.

Step 2. Each researcher involved reviewed case by case the tweets and Facebook posts to identify whether they provide links with evidence of social impact or not. If the researcher considers there to exist evidence of social impact, he or she introduces the value of 1into the column, and if not, the value of 0.

Stride 3. Once all the researchers have finished this pace, the next footstep is to export the excel canvas to SPSS to extract the kappa coefficient.

Footstep 4. Data Analysis Ii. The following footstep was to analyse instance by case the tweets and Facebook posts identified as providing linkages with data of social bear upon and classify them equally quantitative or qualitative show of social impact.

Step 5. The interaction received was analysed because this determines to which extent this show of social bear upon has captured the attending of citizens (in the form of how many likes, shares, or retweets the post has).

Pace half-dozen. Finally, if bachelor, the profile descriptions of the citizens interacting through retweeting or sharing the Facebook mail were considered.

Step 7. SICOR was calculated. It could exist applied to the consummate sample (all data projects) or to each project, as nosotros volition see in the next section.

Results

The full number of tweets and Fb/posts collected from the 10 projects is v,350. Later the content analysis, we identified 23 tweets and Facebook posts providing linkages to data about social impact. To answer to the research question, which considered whether in that location is evidence of social impact shared by citizens in social media, the answer was affirmative, although the coverage ratio is low. Both Twitter and Facebook users retweeted or shared prove of social impact, and therefore, these two social media networks are valid sources for expanding knowledge on the assessment of social impact. Table ten shows the social touch on coverage ratio in relation to the full number of messages analysed.

The assay of each of the projects selected revealed some results to consider. Of the 10 projects, seven had bear witness, but those projects did not necessarily take more than Tweets and Facebook posts. In fact, some projects with fewer than lxx tweets and 50 Facebook posts have more evidence of social impact than other projects with more than than 400 tweets and 400 Facebook posts. This result indicates that the number of tweets and Facebook posts does non make up one's mind the beingness of prove of social impact in social media. For example, projection 2 has 403 tweets and 423 Facebooks posts, but it has no bear witness of social bear on on social media. In contrast, project nine has 62 tweets, 43 Facebook posts, and ii pieces of prove of social impact in social media, every bit shown in Table xi.

The ratio of tweets/Fb posts to bear witness is 0.43%, and information technology differs depending on the project, every bit shown below in Table 12. There is i project (P7) with a ratio of 4.98%, which is a social touch on coverage ratio college than that of the other projects. Adjacent, a group of projects (P3, P9, P10) has a social affect coverage ratio between 1.41% and ii,99%.The next slot has 3 projects (P1, P4, P5), with a ratio between 0.xiii% and 0.46%. Finally, in that location are three projects (P2, P6, P8) without any tweets/Fb posts evidence of social bear on.

Because the three strategies for obtaining data, each is related differently to the evidence of social impact. In terms of the social impact coverage ratio, as shown in Tabular array 13, the about successful strategy is number 3 (searchable inquiry results), as information technology has a relation of 17.86%, which is much higher than the ratios for the other 2 strategies. The 2nd strategy (acronym search) is more than effective than the start (profile accounts),with one.77% for the former as opposed to 0.27% for the latter.

Once tweets and Facebook posts providing linkages with information near social affect(ESISM)were identified, we classified them in terms of quantitative (QUANESISM) or qualitative evidence (QUALESISM)to determine which type of evidence was shared in social media. Table 14 indicates the amount of quantitative and qualitative evidence identified for each search strategy.

Word

Outset, the results obtained indicated that the SISM methodology aids in calculating the social touch coverage ratio of the enquiry projects selected and evaluating whether the social impact of the corresponding research is shared by citizens in social media. The social touch coverage ratio applied to the sample selected is low, but when nosotros analyse the SICOR of each projection separately, we tin observe that some projects have a higher social bear on coverage ratio than others. Complementary to altmetrics measuring the extent to which inquiry results reach out society, the SICOR considers the question whether this process includes evidence of potential or real social bear upon. In this sense, the overall methodology of SISM contributes to advancement in the evaluation of the social affect of research past providing a more precise approach to what we are evaluating.

This contribution complements electric current evaluation methodologies of social impact that consider which improvements are shared by citizens in social media. Exploring the results in more than depth, information technology is relevant to highlight that of the ten projects selected, in that location is 1 inquiry project with a social impact coverage ratio college than those of the others, which include projects without any tweets or Facebook posts with evidence of social impact. This project has a higher ratio of testify than the others because show of its social impact is shared more is that of other projects. This also means that the researchers produced evidence of social touch and shared information technology during the project. Another relevant result is that the quantity of tweets and Fb/posts collected did not determine the number of tweets and Fb/posts found with evidence of social impact. Moreover, the assay of the research projects selected showed that at that place are projects with less social media interaction only with more tweets and Fb/posts containing evidence of social media impact. Thus, the number of tweets and Fb/posts with evidence of social impact is not adamant by the number of publication messages nerveless; information technology is determined by the type of messages published and shared, that is, whether they contain testify of social bear on or not.

The second main finding is related to the effectiveness of the search strategies defined. Related to the strategies carried out nether this methodology, one of the results found is that the most effective search strategy is the searchable research results, which reveals a higher pct of bear witness of social impact than the own account and acronym search strategies. However, the use of these three search strategies is highly recommended because the combination of all of them makes it possible to place more than tweets and Facebook posts with prove of social bear on.

Some other outcome is related to the blazon of bear witness of social impact found. There is both quantitative and qualitative evidence. Both types are useful for agreement the type of social impact achieved by the corresponding inquiry projection. In this sense, quantitative evidence allows us to sympathise the improvements obtained by the implementation of the inquiry results and capture their affect. In contrast, qualitative evidence allows the states to securely understand how the resultant improvements obtained from the implementation of the research results are evaluated by the end users by capturing their corresponding directly quotes. The social impact includes the identification of both real and potential social bear on.

Conclusions

After discussing the master results obtained, we conclude with the following points. Our written report indicates that there is incipient evidence of social impact, both potential and real, in social media. This demonstrates that researchers from different fields, in the nowadays case involved in medical research, public health, brute welfare and genomics, are sharing the improvements generated by their enquiry and opening up new venues for citizens to interact with their piece of work. This would imply that scientists are promoting non but the broadcasting of their research results simply also the evidence on how their results may lead to the improvement of societies. Considering the increasing relevance and presence of the broadcasting of research, the results betoken that scientists still need to include in their dissemination and communication strategies the aim of sharing the social impact of their results. This implies the publication of physical qualitative or quantitative bear witness of the social affect obtained. Because of the inclusion of this strategy, citizens will pay more attention to the content published in social media because they are interested in knowing how science can contribute to improving their living weather and in accessing crucial data. Sharing social impact in social media facilitates admission to citizens of different ages, genders, cultural backgrounds and teaching levels. Nonetheless, what is nearly relevant for our statement here is how citizens should too be able to participate in the evaluation of the social affect of research, with social media a peachy source to reinforce this democratization process. This contributes not merely to greatly improving the social bear on assessment, as in addition to experts, policy makers and scientific publications, citizens through social media contribute to making this assessment much more accurate. Thus, citizens' contribution to the dissemination of evidence of the social bear upon of research yields access to more various sectors of society and information that might be unknown by the research or political community. Two future steps are opened here. On the one hand, it is necessary to farther examine the profiles of users who interact with this evidence of social bear on considering the limitations of the privacy and availability of contour information. A 2nd future task is to advance in the articulation of the role played by citizens' participation in social bear upon assessment, as citizens can contribute to electric current worldwide efforts past shedding new light on this process of social impact cess and contributing to making science more relevant and useful for the most urgent and poignant social needs.

Supporting information

S1 File. Interrater reliability (kappa) result.

This file contains the SPSS file with the upshot of the calculation of Cohen's Kappa regards the interrater reliability. The word certificate exported with the obtained result is too included.

https://doi.org/10.1371/journal.pone.0203117.s001

(ZIP)

S2 File. Data collected and SICOR calculation.

This excel contains four sheets, the first 1 titled "data collected" contains the number of tweets and Facebook posts collected through the three defined search strategies; the second sheet titled "sample" contains the sample classified past project indicating the ID of the message or lawmaking assigned, the type of message (tweet or Facebook post) and the codification done by researchers being one (is testify of social impact, either potential or real) and 0 (is not testify of social impact); the tertiary sheet titled "prove plant" contains the number of type of evidences of social impact founded by project (ESISM-QUANESIM or ESISM-QUALESIM), search strategy and type of message (tweet or Facebook posts); and the last sail titled "SICOR" contains the Social Touch on Coverage Ratio calculation by projects in one tabular array and type of search strategy done in another one.

https://doi.org/x.1371/journal.pone.0203117.s002

(XLSX)

Acknowledgments

The research leading to these results received funding from the 7^th Framework Programme of the European Committee under Grant Understanding n° 613202. The extraction of available information using the listing of searchable keywords on Twitter and Facebook followed the ethical guidelines for social media research supported by the Economical and Social Inquiry Council (UK) [36] and the University of Aberdeen [37]. Furthermore, the research results accept already been published and made public, and hence, there are no upstanding issues.

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