The role of social networks in communication in the scientific research community

THE ROLE OF SOCIAL NETWORKS IN COMMUNICATION
IN THE SCIENTIFIC RESEARCH COMMUNITY

Sonia Martin-Gomez* , Angel Bartolome Muñoz de Luna

Universidad San Pablo CEU, CEU Universities (Spain)

Received August 2023

Accepted October 2023

Abstract

Social networks have grown rapidly in recent years, enabling the application of social web technologies to the scientific process and creating platforms that enhance communication between researchers. The aim of this research is to go one step further and investigate whether the use of more general social networks, such as Twitter (currently X) or Facebook, is also becoming more widespread for scientific research, thus contributing to the visibility of scientists and their collaborative networks. Social media analysis is carried out using the Brandwatch platform to assess the use of generalist social networks in research, and compared with the use of scientific social networks through an online survey of university professors. The resulting conclusions show that scientific mentions in networks are rare and that, despite the importance of researchers having a profile in a social network, which allows them to give greater visibility to their results and receive feedback from their colleagues, many of them are still unaware of its usefulness.

 

Keywords – Social network, Scientific network, Research, Dissemination, Communication.

To cite this article:

Martin-Gomez, S., & Muñoz de Luna, A.B. (2024). The role of social networks in communication in the scientific research community. Journal of Technology and Science Education, 14(2), 291-305. https://doi.org/10.3926/jotse.2361

 

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    1. 1. Introduction

Social networks are in fashion, they are growing every year and society is clear about their use to be in contact with a multitude of people, to reflect moods, to share experiences, lifestyles and brands... These different uses make us wonder to what extent generalist social networks can be used in academic research.

According to the Digital 2023 Global Overview Report, the number of network users will grow by 227 million in 2021, reaching a total of 4.7 billion by early July 2022. The global number of active “user identities” on social networks will reach 4.8 billion by April 2023. Current trends suggest that by July 2023, two-thirds of the world’s population will be online and the number of social network users will be equivalent to 60% of the world’s population (DataReportal, Meltwater & We are Social, 2023).

In 2023, around 85% of Spanish internet users accessed social media platforms. In 2022, the country registered more than 40 million social media users, making it one of the largest social media markets in Western Europe (IAB Spain, 2023).On the other hand, according to a survey conducted in Spain by IAB Spain in March 2023 and reflected in Figure 1, WhatsApp was the favourite social media app for 32% of respondents. The messaging app has lost its position compared to the previous year, while TikTok and Instagram were the networks that grew the most in user preference since 2022.

 

Figure 1. Most used social media platforms in Spain (from 2018 to 2023)

In terms of how people use social media, according to the Digital News Report 2023, people are now 2.5 times more likely to turn to social media for news than to print newspapers and magazines (Reuters Institute, 2023).

In short, it can be argued that social media is now more than just a means of communication, as it has evolved to create personal and social connections, as well as influencing business and politics. However, it should not be forgotten that researchers also interact and communicate to share results, projects, resources, information and documentation in scientific social networks (Zapata-Ros, 2011), which also helps them to communicate with other research colleagues. Therefore, the aim of this research is to find out whether, in addition to purely scientific social networks such as Research Gate or Methodspace, the most popular are also used at the scientific level.

The fact that research on scholarly communication took on new importance from the mid-nineties can be attributed to a progressive restructuring of the scholarly communication system together with a rapid growth of information technology, networking and electronic publishing (Borgman 2000). Communicating and disseminating science is part of the research process, which does not end in the scientific article, but in the transmission of research results to the public, making it possible for many academic works to provide practical advice on how to approach scientific communication from different perspectives, such as social networks, and originality in this communication (Cooke, Gallagher, Sopinka, Nguyen, Skubel, Hammerschlag et al., 2017; Pérez-Rodríguez, González-Pedrás & Alonso-Berrocal, 2018).

That is why in its Recommendation on Open Science, the United Nations Educational, Scientific and Cultural Organisation (UNESCO) defines Open Science as an inclusive construct that brings together diverse movements and practices to make multilingual scientific knowledge openly available, accessible and reusable by all, to strengthen scientific collaboration and information sharing for the benefit of science and society, and to open up the processes of creation, evaluation and communication of scientific knowledge to societal actors beyond the traditional scientific community (UNESCO, 2021).

González-Suárez (2006) ensures that scientific communication, in addition to being the process of transmission and dissemination of knowledge, constitutes the form through which the results derived from research activity are incorporated into human knowledge, which is transcendent due to the falsification to which all new knowledge must necessarily be submitted and due to the reproducible nature of science.

In this sense, all the possibilities of communication through the Internet allow the exchange of opinions and knowledge between students, teachers, specialists, etc., and should promote the development of scientific, creative and expressive skills, as well as the cultivation of positive attitudes towards interpersonal communication (Fox & Wilson, 2009).

Therefore, in recent years, according to Nassi-Caló (2015), the use of social networks in scientific communication has increased, and specific platforms for interaction and information transfer between researchers have been created. These platforms are used in the same way as general networks, but only involve teachers, who usually participate in research projects and currently constitute one of the main bets by the media to attract audiences interested in scientific content (Harmatiy, 2021). The coronavirus pandemic has also contributed to this development, which caused us to rethink how scientific actions are being communicated and what are the means by which this message can be better reached (Diviu-Miñarro & Cortiñas-Rovira, 2020).

Social networks are like virtual laboratories because they offer all the services that a research group needs: a simple communication system, the possibility of using different ways of sharing resources, the storage of documentation in a profile and the creation of discussion forums (Equihua, 2016).

Science 2.0 can be seen as the application of social web technologies to the scientific process. Codina (2009) highlights two basic ideas that favour the use of Web 2.0 in science: science is communication and science is collaboration. Communication and collaboration are the aspects to be highlighted in any social network, hence the importance of its use in research.

Social networks have also generated new metrics for measuring science, so the term altmetrics can be defined as an alternative metric that complements traditional metrics, insofar as it allows the counting of citations or mentions of global academic production to be fairer and more egalitarian, giving rise to a science that is also more democratic, since it is characterised by the creation and use of new indicators that explore the properties of measurements based on social networks, acting in the same way as traditional impact indicators (Vanti & Sanz-Casado, 2015).

For this reason, it is now necessary for every researcher-teacher to have at least one profile in a scientific social network, which allows them to make their research activity known to the rest of their colleagues and improve its visibility, increasing the chances of being cited by other researchers in the same field.

The possibilities offered by a scientific social network are varied, but we must be participative and take into account the importance of both collaboration and participation, as it will also be useful for the scientist to receive feedback on his or her work (Santana-Arroyo, 2010).

With regard to generalist social networks, they can be considered as “associations of people linked by heterogeneous motives, forming a structure composed of nodes linked by more than one type of relationship” (Hernández-Requena, 2008: page 30). In other words, there may be several reasons why several people are linked in this network, which is why it is more feasible to reach a larger number of people than just with a scientific social network where people are linked by scientific interests.

However, although these more generalist social networks are used on a massive scale, they are rarely used for scientific and/or didactic purposes, and in this sense there is still a long way to go to enable more efficient communication and collaboration between students and teachers/researchers, or even between researchers themselves. In conclusion, it is academic social networks that appear as professional and social networks of researchers, combining the characteristics of social networks with the publication of studies, all adjusted to the needs and behavior of academic researchers (Ovadia, 2014).

The following table (Table 1) summarises the main advantages and disadvantages of the use of generalist social networks by researchers, and although it may seem that the advantages and disadvantages are close together, the disadvantages can be reduced, since in an environment as volatile as the one we live in, speed can be a reward, which should not compromise quality or thoroughness due to proximity, both of which become a virtue.

Furthermore, it should be pointed out that the analysis of social networks is a complex and rigorous process that requires knowing how to identify false information, any type of publicity or negative effects that may affect the final results of the proposed research, but also knowing how the platform to be used for this analysis works.

In view of this, the main objective of this research is to discover the use of generalist social networks at the scientific scope and the level of development of scientific networks, for which it aims to answer the following questions

  1. 1.Are generalist social networks used in research? 

  2. 2.Among the generalist social networks, which are the most used for the dissemination of science? 

  3. 3.Do researchers know about scientific networks, how do they perceive them and how do they use them? 

     

Pros of social media

Cons of social media

Immediate communication system

Registration at no financial cost

Facilitate interaction between users

Allow discussion and feedback

Connect people from any country.

Bring together colleagues of different scientific and academic status.

They allow research results to reach any profile.

A quick search for information

Speed of publication may be more important than quality

They generate competition among scientists to be pioneers in disseminating information.

They can spread hoaxes or “fake news”.

Giving more importance to the result of the research than to other aspects.

Bringing science closer to society can make the information less rigorous.

Possible lack of respect in the absence of moderators.

Transfer of users’ personal data in exchange for advertising.

Table 1. Advantages and disadvantages of social networks for the scientific community
(
Fernández-Bayo, Menéndez, Fuertes, Milán & Mecha, 2019)

2. Methodology

The methodology of the proposed research is based on the study of general social networks in what is known as social listening, originally a practice of monitoring what customers say about a brand in different online spaces. Social listening works not only with the perception that users have on networks about a specific brand, but in general at any point of online contact between the consumer and the brand.

Most social media data is stored in a structured or unstructured format. Structured data adheres to standardised and well-defined data formats, while unstructured data is often more difficult to process because the format is not predefined, such as a Facebook post (Hartman 2020). A variety of social technologies can be used to analyse this data: “social listening platforms”, “social advertising technology” and “social suites”. Social listening platforms are used to collect, manage and analyse social media data. Social advertising technology is used to manage and measure social media advertising. Social suites combine many of the capabilities of social technologies into a single platform. They are used to perform tasks such as data collection and analysis, and publishing customer communications (Liu & Dawson, 2021).

According to the report, “Forrester Wave: Social Listening PlatformsTM (SLP), Q4 2020”, which provides a comprehensive assessment of the leading SLP vendors, Brandwatch, the consumer research platform used in this research, which was assessed alongside nine other SLP vendors (Digimind, Linkfuence, ListenFirst, Meltwater, NetBase, Quid, Sprinklr, Synthesio, Talkwalker, and Zignal Labs), is a leader in the platform market, scoring highest in the areas of strategy and market presence (Liu & Dawson, 2020).

The process of social media analysis is typically divided into four phases (Stieglitz, Mirbabaie, Ross & Neuberger, 2018):

  • Discovery: identification of content and its corresponding keywords, hashtags, etc., which contribute to defining the objectives of the analysis and the main hypotheses to be tested. 

  • Monitoring: identifying data sources and data collection. 

  • Preparation: Prepare the data for the subsequent analysis. 

  • Analysis: Applying various analytical methods and techniques to the data set prepared to answer the questions posed in the discovery phase. 

In this research, as shown in Figure 2, the same steps proposed by Stieglitz are followed, with the addition of another step related to subsequent implementation, understood as the need to effectively communicate the results of social network analysis.

 

Figure 2. Mapping of Brandwatch’s key functions to the network analysis process framework

The discovery phase uses Brandwatch Search, an artificial intelligence-based search engine that uses sophisticated natural language processing techniques. In this case, the search is linked to the use of social networks in research. In the follow-up phase, the so-called query is formed, which refers to the set of words that allow information to be obtained from the platform’s systems. For this purpose, Boolean operators were used to combine the searched concepts and to refine the results to be obtained, as shown below:

 

This query returns 3,980 mentions in the last 30 days on the day of the study alone, after filtering by language (Spanish), but searching all over the world. Therefore, tools are needed to segment and filter this information, including a test preview to immediately evaluate the type of mentions retrieved from the current query logic, favouring the intended social analysis; in this search, it was decided to eliminate websites that mention the terms searched for but are not related to the objective of the study.

Finally, the query is maintained, filtered by language, invalid sites are eliminated and a date range of one year is marked in order to analyse whether the evolution of the content under study follows a certain pattern.

In the last two stages, the results obtained are analysed and acted upon through the use of dashboards, which monitor and visualise the key indicators.

This network analysis is based on a sampling rate of 100%, with an estimated 3,314 mentions per month.

This study was contrasted with a short survey, using a Google Forms form, of researchers and professors at a university in Madrid, guaranteeing the anonymity of the responses obtained, which made it possible to compare the evolution of the researchers’ own networks and their perceptions of them with the results obtained through social listening. The questionnaire consisted of ten simple questions, mostly dichotomous and some with a Likert scale to measure the degree of agreement or disagreement, with an estimated average response time of eight minutes. It was conducted between mid-May and June 2023, with a total of 148 responses. The link to the survey is: https://forms.office.com/e/hLMHkPtV2K

3. Analysis and Results

The results derived from the social listening research, shown in Figure 3, show little volatility, as only on 22 November and now are there more mentions in networks about the use of networks in research, reaching 800 and 1000 mentions/day respectively, with an average of 100 mentions per day. However, this figure is very low if we compare it, for example, with the number of mentions on the same dates of the query on the use of artificial intelligence in research and higher education, which reached 19,962 mentions, with peaks on some days of up to 3,047 mentions/day.

In Figure 4, we analysed which sources are leading this social conversation, showing that the social network Twitter (X) clearly leads the volume of conversations about the search, with more than 100 days in the 12 months analysed in this research.

 

Figure 3. The volume of mentions by day

 

Figure 4. Media with the highest number of mentions per day

Figure 5 shows the trending topics related to the query studied, i.e. the number of mentions of the terms related to the study; the aim is to be able to deduce which keywords are driving these conversations, in order to better understand what underlies each conversation. In this way, we have chosen to use word cloud technology, which not only extracts the keywords in the conversations, but also identifies those with an increasing trend on the right-hand side of the lower horizontal axis, as opposed to those with a decreasing trend on the left-hand side of the same lower horizontal axis, with terms such as email, projects, information or person appearing, but without there being some more important than others, which manage to group together almost all the trending topics and which could be similar to the keywords listed for this work.

Figure 6 shows the top themes or most repeated mentions in the year studied, highlighting terms such as “people”, “analysis” or “information”, which also appeared as trend themes.

 

Figure 5. Trend topics