An Online Scientific Twitter World: Social Network Analysis of #ScienceTwitter, #SciComm, and #AcademicTwitter

Abstract

Understanding who makes up online affinity spaces as well as how information flows within those spaces is important as more people access news, research topics, collaborate with others, and entertain themselves. During a month-long period in summer 2021, we collected 100,000 tweets from 53,311 Twitter users who used the hashtags #ScienceTwitter, #SciComm, and #AcademicTwitter. We then classified users and determined the type of social network they formed. Scientists, the public, and educators formed this affinity space. They built connections by initiating activities and interacting with others, which created a Community Clusters social network structure, characterized by several medium-sized groups of closely connected users and a fair number of isolates. All three categories of people were in positions of influence in this network leading and controlling the conversations. The results show that scientists, the public, and educators share the space and contribute to communication in this online world. This research is important as it illustrates that online affinity spaces about scientific topics are not solely spaces for scientists to communicate but rather act as spaces where people with varied expertise can exchange ideas and learn from one another.

1. Introduction

Understanding who makes up online social worlds in microblogging spaces such as Twitter (now known as X), as well as how information flows within those communities is important as more people use these platforms to access news, research topics, collaborate with others, and entertain themselves. In recent years, scholars and scientists have used the microblogging service Twitter as a communication platform to share their work and opinions. However, with Twitter’s sale to Elon Musk in 2022, many people, including scientists, journalists, and educators, moved to other platforms in rebuttal of Twitter’s increasing spread of misinformation and limited ability to rectify (or interest in rectifying) such misinformation (Stokel-Walker, 2023; Hickey et al., 2025). Prior to this scientific diaspora, Twitter was a rich vein of research on science communication and education. Since these changes at Twitter, scientists, journalists, and educators have turned to other microblogging platforms, such as Bluesky and Mastodon (Biever, 2025; Mallapaty, 2024). In many ways, these platforms replicate the social, scientific communities that flourished on Twitter, providing people interested in science with the ability to communicate with one another (reply), indicate their support of or interest in content (like or repost), and create their own content (post). Thus, while the platforms themselves change, the affordances of the platforms and reasons people use such platforms remains the same: to quickly acquire, react to, and learn from others, and share knowledge and information (Carpenter et al., 2024; Ebersole et al., 2024). Because of similar usage patterns, reasons for use, and platform affordances, we argue that data sets from Twitter can act as proxies for other social microblogging spaces.

Much of the previous research on Twitter focused on specific user groups and their communication practices. For example, N. M. Lee et al. (2020) analyzed the science communication practices of a for-profit company. Bex and colleagues (2019) attempted to define the members of a paleontological community on Twitter. Researchers have also studied how teachers use Twitter: to teach and share knowledge (Richter et al., 2024; Manca & Ranieri, 2016; Rosenberg et al., 2016), build networks and collaborate (Greenhow & Askari, 2017), and develop their careers (Carpenter & Krutka, 2015; Malik et al., 2019; Prestridge, 2019; Déchène et al., 2024).

While there is a rich vein of literature on the digital social lives of educators, additional research is needed to delineate the composition of the social, scientific world on Twitter across a wide range of scientific topics, and how information is transmitted and controlled in this online world. From a pedagogical perspective, microblogging platforms like Twitter are important. As informal, digital learning spaces, they allow for fast and easy exchange of information amongst people from varied backgrounds, which can lead to increased social ties (Lundgren et al., 2024) as well as learning (Demir, 2024). This study analyzed how people discuss scientific topics on Twitter. We collected tweets about scientific topics under three hashtags, classified the users who created these tweets, and studied these users and the social network they formed. Our study has two main contributions: (1) improving understanding of scientific communication on social media platforms like Twitter and (2) providing evidence of people with varied experiences and expertise interacting within an online affinity space.

2. Conceptual Framework

Affinity spaces are unique spaces that unite people with common interests or passions (Gee, 2004). Unlike communities of practice that emphasize membership or apprenticeship, Gee (2004) uses the term “space” to define another social configuration that focuses on the interaction when people engage and learn. The great majority of research concerning affinity spaces comes from educational research. We see the potential of using affinity spaces as social media sites, as they can act as informal learning spaces (e.g., Lundgren & Couch, 2024; Ocon et al., 2021). We define informal learning based on Rogoff et al.’s (2016) definition of informal learning as “nondidactic, is embedded in meaningful activity, builds on the learner’s initiative or interest or choice, and does not involve assessment external to the activity” (p. 356). Applying the framework of affinity spaces to social media sites like Twitter is useful as people form communities around hashtags, trending topics, or shared interests: Essentially, people are building on their own initiative and choices that are meaningful to them. Twitter and other social networking sites enable informal learning and knowledge exchange and act as fluid, interest-driven spaces where expertise and participation are decentralized. In Gee’s (2012) description of affinity spaces, people gathered to interact with each other, to share practices and ideas based on a common interest. Gee further identified 11 features that can indicate the degree to which a space is an affinity space. Some of these features include the following: A common endeavor is primary, a common space is shared inclusive of different levels of experience and expertise, different kinds of knowledge are encouraged, and different routes and forms of participation exist. Affinity space features are flexible in that some spaces have more of the features than others.

Affinity spaces are common in today’s world. Fans of all things (e.g., movies, comic books, TV shows, video games, various lifestyles) create and maintain affinity spaces (Min et al., 2019; Shafirova et al., 2020; Barany & Foster, 2021; Dynel & Ross, 2022). Many businesses organize such spaces (Gee et al., 1996). Social activists also often organize themselves and others in terms of affinity spaces (Pour-Khorshid, 2018). Scientists from many different disciplines connect with others around the globe in a variety of ways, gradually taking on more of the features of an affinity space (Sharma & Land, 2018; Fontaine et al., 2019). These spaces are not just physical spaces; they can also be virtual spaces on the web.

Neely and Marone (2016) explored the participation of people with similar interests in informal social and learning activities by identifying 11 affinity space features in specific physical spaces: jam band parking lots. Some researchers have explored affinity spaces on social media, such as Twitter and Reddit, and other social networking sites. Greenhalgh et al. (2020) explored a teacher-focused Twitter hashtag, #michED, to determine whether different learning spaces exist in chat and non-chat tweets using the criteria of volume, content, interaction, and portal of the affinity space. Marcelo-Martínez and Marcelo (2025) analyzed how a hashtag on Twitter allows a space of affinity to be created that facilitates teachers’ learning. Na and Staudt Willet (2022) explored teachers’ early career development on Reddit posts. Staudt Willet (2019) explored how and why educators use Twitter affinity space generated by #Edchat. Staudt Willet and Carpenter (2020a, 2020b) also explored the change and continuity of two teaching-related subforums on Reddit and the contributions and interactions of four teaching-related subforums, respectively. Sharma and Land (2018), on the other hand, focused on knowledge sharing and interaction patterns in the discourse of a diabetic online affinity space. These studies suggest that affinity spaces can be identified in social media such as Twitter, and that affinity space features of specific topics will be more pronounced (e.g., groups of fans or groups of educators). For this reason, affinity spaces serve as the conceptual framework to determine the extent to which this online, scientific, social Twitter space can be characterized as an affinity space. Thus, we align with previous educational research into questions to determine who is involved in an online, scientific spaces centered on hashtags.

3. Background

3.1. Science Communication on Twitter

Science communication is multi-faceted, with myriad definitions. For this work concerning social media, we use the definition of science communication laid out by the United States’ National Academy of Science: “the exchange of information and viewpoints about science to achieve a goal or objective…or gaining greater insight into diverse public views and concerns about science” (National Academies of Sciences, 2017, p. 2). Moukarzel et al. (2020) found that Twitter created an opportunity space for science communication in which the scientific community, including researchers, can communicate science to the public and for the public to communicate with scientists. The science community faces a wide range of identified challenges: Influencers have less heterogeneous relations than companies; they engage in more activity but reach fewer unique individuals, and they primarily use networks for research, announcements, and commercial purposes (Moukarzel et al., 2020). Anderson and Huntington (2017) highlighted how social media discussions have the potential to influence the way people engage with science. N. M. Lee et al. (2020) found that companies communicate about various topics related to health and the environment that can have substantial implications at the individual and society levels. McNeil et al. (2024) provided insight into how paleontological art spread among audiences under the Twitter hashtag #SciArt and explored the enduring popularity of dinosaur-themed works in science communication, while Wang et al. (2025) described climate actions and actors. These studies provide a basis for further research into the effects of science communication on social media.

Previous research has shown that educators use social media to teach and share knowledge while building networks and collaboration. Manca and Ranieri (2016) presented potentials and obstacles for higher education teachers using social media in their teaching practices. Greenhow and Askari (2017) pointed out that social media seems to help strengthen interaction and networking among teachers, students, and parents. In particular, teachers have used Twitter in classrooms in various studies, across disciplines and activities with different durations (Tang & Hew, 2017). Rosenberg et al. (2016) found that teachers are highly active on education-specific hashtags. Through knowledge sharing and network building, teachers use Twitter to enhance career development and professional learning (Carpenter & Krutka, 2015; Malik et al., 2019; Prestridge, 2019).

Research on Twitter social networks is most often conducted through the analysis of Twitter users—specifically, their roles and the networks they form. For instance, Bex et al. (2019) defined the members of a paleontology social space from three levels (structure, category, and type) and how members from different categories such as scientist and public were sharing messages. Moukarzel et al. (2020) categorized influencers into three categories: scientific community, interested citizens, and for-profit companies. Studies that examined Twitter through Twitter users found that different categories of users all play a role in information sharing. In examining user networks, Ahmed et al. (2020) found that an isolated group and a broadcast group constituted the two largest social network structures through an analysis of 5G and COVID-19 conspiracy theories on Twitter. Further, Bhandoria et al. (2021) confirmed the network of “#IGCS2020” on Twitter as a community network shape with elements of a broadcast network (Smith et al., 2014). Categorizing and analyzing Twitter users can help understand who the people are in the Twitter online social space.

3.2. Research Questions

Previous research has been conducted on specific scientific topics or users, and user categorization provides insights into who people are within an online social space. Additionally, the analysis of social networks can reveal how the information is transmitted between users. In this research, we analyze scientific topics on Twitter, including who is in the affinity space (Twitter users) and how information spreads through it and influences user activity (social network analysis). We ask the following research questions:

• RQ1: Who is involved in the affinity space of #ScienceTwitter, #SciComm, and #AcademicTwitter?
• RQ2: How is scientific information contributed and distributed in this affinity space?
• RQ3: How is the flow of information in the Twitter network influenced and controlled by different types of users?

4. Materials and Methods

This study aimed to identify members of an affinity space centered on scientific topics and analyze the wider conversations that occurred in social, scientific Twitter spaces to determine the social network structure. In this study, we collected Twitter data between June and July 2021 and then processed it to obtain a new database containing more attributes, including Twitter usernames, biographies, and their relationships. We classified users and visualized the social network based on the new dataset. This process consists of three stages: data collection, data coding, and data analysis.

4.1. Data Collection

Social networks are created whenever people interact directly or indirectly with other people, institutions, or artifacts; social network analysis can visualize complex relationships through graphs (Hansen et al., 2020). Researchers used social network analysis to examine topics around Twitter, identifying the centers of information dissemination and making recommendations for the promotion of scientific knowledge (Ahmed et al., 2020; Brajawidagda, 2012; M. K. Lee et al., 2017; Milani et al., 2020). Smith et al. (2014) suggested mapping social media networks can enable a better understanding of the variety of ways individuals form groups and organize online. They illustrated six different structures of connections around different kinds of topics on Twitter: polarized crowd, tight crowd, brand clusters, community clusters, broadcast network, and support network (Smith et al., 2014). The characteristics of each rely on the ways in which people connect to one another. For instance, in a broadcast network, users mainly connect to one main other user (a hub), while in a community cluster network, users are highly interconnected to one another and do not rely on information or connecting with only one main user.

Researchers have also conducted content analysis, which focuses on tweet categorization and its effect on message dissemination. For example, Bex et al. (2019) and Lundgren et al. (2022) classified five types of messages: Information, News, Opportunity, Research, and Off-Topic, finding that the most successful, engaged with, and far-reaching expression of scientific practice were Information posts. Additionally, Bombaci et al. (2016) defined the type of session in a conservation science conference and found Twitter effectively conveyed conservation science to a diverse and somewhat unexpected audience beyond the conference. Su et al. (2017) divided tweets into three categories based on the communication function: Information, Participant, and Community, and determined what type of communication they were and what their purpose was. Toupin et al. (2022) additionally used machine learning to discover user types who tweeted academic research about climate change. These studies show that defining kinds of information sharing can reveal what kind of content on Twitter is highly effective (i.e., highly shared, liked, retweeted, or replied to).

The original dataset was collected from three hashtags: #ScienceTwitter, #SciComm, and #AcademicTwitter. These hashtags were chosen using a hashtag tracking software called hashtagify.me that allowed for people to identify popular hashtags and track their popularity over time. An additional parameter for choosing hashtags came from our experience as researchers who have used Twitter as academics for the past ten years. Netlytic, a browser-based text and social network analysis service (Gruzd, 2016), was used to schedule a sampling of the Twitter public search API (application programming interface) every 15 min for a one-month period (June–July 2021). This process resulted in a dataset of 100,000 tweets with several attributes, including TweetID. We then imported the TweetIDs from the dataset into NodeXL (Smith et al., 2010), a Microsoft Excel Add-In that allows for researchers to collect and analyze more attributes than Netlytic. Using NodeXL, the original 100,000-tweet dataset was associated with 53,311 Twitter users. This transformed dataset included more attributes, such as username, description, and user relationship (i.e., mentions, retweets, replies to, etc.). The NodeXL dataset was used for social network visualization and analysis.

4.2. Data Coding

Two researchers manually classified 1000 Twitter users to prepare the data for machine learning classification of all users. Based on NodeXL’s feature of randomly sorting usernames, we selected the first 1000 users for classification. We used a taxonomy called paleontological identity taxonomy (PIT) (Lundgren et al., 2018) for our manual coding process. The PIT is a tool that can be used to classify members of digital social spaces. It uses members’ self-descriptions (i.e., Twitter user biographies) to classify them into three different levels: structure, category, and type. The main unit of analysis in this study was at the category level, defined as a general representation of an entity’s identity. The PIT has been validated and used in multiple studies (e.g., Lundgren et al., 2024; Lundgren & Couch, 2024; Lundgren & Crippen, 2024; Lundgren et al., 2022; Ocon et al., 2021); we saw potential to expand its use to wider scientific discourse, instead of focusing on a singular scientific discipline. To code for category, users are divided into Public, Scientists, or Education and Outreach (

Table 1. Categories within the paleontological identity taxonomy (PIT).

Category	Definition
Education and Outreach	Any entity reference to working in a K-12 setting; as a teacher, lecturer, or in a classroom; in/as a museum or the main focus of the account is education; reference to providing some kind of advocacy or promotion of diversity, equity, and inclusion efforts or providing services to populations that might not otherwise have access to those services (i.e., outreach)
Scientist	Any entity that uses a scientific domain to classify themselves, use of “-ist”; students (graduate or undergraduate) using their major; centers, institutes, and research groups are included if they indicate their audience to be other scientists.
Public	Any entity that does not meet the definition of Scientist or Education and Outreach.

). The first and second authors discussed any discrepancies to consensus (Patton, 2002).

4.3. Data Analysis

To answer research question 1 (Who is involved in the affinity space of #ScienceTwitter, #SciComm, and #AcademicTwitter?), all 53,311 users were classified into one of three categories. Due to the large number of Twitter users in the whole dataset, the dataset was deemed appropriate for computational analyses. We first tried two different methods for classification: RapidMiner and building dictionaries. RapidMiner (v. 10.3.1) is software that can call different algorithms for data mining and processing. When we fed the sampled usernames into RapidMiner, it had a high accuracy rate (0.96) in the Scientist category but misclassified users in other categories as Scientist. We also built dictionaries for classification (Côté & Darling, 2018; Li et al., 2019; Toupin et al., 2022; Walter et al., 2019), achieving high accuracy rates for Scientist (0.95) and Education and Outreach (0.87) but not Public (0.46). We postulate that the low accuracy can be accounted for due to the high degree of overlapping words in their dictionaries.

Next, we experimented with multiclass classification for categorization (Grandini et al., 2020). In this, each piece of sampled text can only be labeled as one class. Several researchers have applied the multiclass classification to the analysis of Twitter user content (Balabantaray et al., 2012; Ceron et al., 2015; Bouazizi & Ohtsuki, 2019; Li et al., 2019; AlSomaikhi & Alzamil, 2020). We used the Python library Scikit-Learn (v. 1.4.2) (Pedregosa et al., 2011) to implement the multiclass classification models. We divided the 1000 manually coded users (407 Scientists, 212 Education and Outreach, 381 Public) into training and test sets (80:20 ratio) to evaluate seven models with 5-fold cross-validation. The models included Logistic Regression, Random Forest, Linear Support Vector Machine, Support Vector Machine, Multinomial Naive Bayes, Stochastic Gradient Descent, and Multilayer Perceptron. We extracted features from user bio text using the term frequency–inverse document frequency (TF-IDF, evaluating the importance of different words in a sentence). Performance metrics included accuracy, precision (ratio of true positives and total positives predicted), recall (ratio of true positives to all actual positives), and F1 score (harmonic mean of precision and recall) for each category and macro (arithmetic mean) and weighted values (mean while considering each class’s support). We selected Stochastic Gradient Descent as the best model for classification, due to good performance across metrics in all classification categories (

Table 2. Classification model performance evaluation.

Model	Category	Precision	Recall	F1-Score	Support
Logistic Regression	Public	0.85	0.53	0.65	74
	Scientist	0.52	0.96	0.67	81
	Education and Outreach	1.00	0.07	0.12	45
	Accuracy	N/A	N/A	0.60	200
	Macro avg	0.79	0.52	0.48	200
	Weighted avg	0.75	0.60	0.54	200
Random Forest	Public	1.00	0.19	0.32	74
	Scientist	0.44	1.00	0.61	81
	Education and Outreach	0.00	0.00	0.00	45
	Accuracy	N/A	N/A	0.48	200
	Macro avg	0.48	0.40	0.31	200
	Weighted avg	0.55	0.47	0.36	200
Linear Support Vector Machine	Public	0.74	0.82	0.78	74
	Scientist	0.66	0.84	0.74	81
	Education and Outreach	0.73	0.24	0.37	45
	Accuracy	N/A	N/A	0.70	200
	Macro avg	0.71	0.64	0.63	200
	Weighted avg	0.71	0.70	0.67	200
Support Vector Machine (SVC)	Public	1.00	0.22	0.36	74
	Scientist	0.44	1.00	0.61	81
	Education and Outreach	1.00	0.02	0.04	45
	Accuracy	N/A	N/A	0.49	200
	Macro avg	0.81	0.41	0.34	200
	Weighted avg	0.77	0.49	0.39	200
Multinomial Naive Bayes	Public	0.70	0.69	0.69	74
	Scientist	0.56	0.86	0.68	81
	Education and Outreach	1.00	0.02	0.04	45
	Accuracy	N/A	N/A	0.61	200
	Macro avg	0.75	0.53	0.47	200
	Weighted avg	0.71	0.61	0.54	200
Stochastic Gradient Descent (SGD)	Public	0.78	0.64	0.70	74
	Scientist	0.71	0.80	0.76	81
	Education and Outreach	0.55	0.60	0.57	45
	Accuracy	N/A	N/A	0.69	200
	Macro avg	0.68	0.68	0.68	200
	Weighted avg	0.70	0.69	0.69	200
Multilayer Perceptron (MLP)	Public	0.60	0.86	0.71	74
	Scientist	0.73	0.65	0.69	81
	Education and Outreach	0.76	0.36	0.48	45
	Accuracy	N/A	N/A	0.64	200
	Macro avg	0.70	0.62	0.63	200
	Weighted avg	0.69	0.67	0.65	200

).

Following the classification of user biographies, we used NodeXL to conduct a social network analysis to visualize the network structure with the Harel–Koren Fast Multiscale algorithm. All users were grouped into clusters using the Clauset–Newman–Moore algorithm (Clauset et al., 2004), which enables the discovery of subgroups within the larger dataset. Nodes represented the users and edges represented five connection types between the users (

Table 3. Connection types in the Twitter network.

Type	Definition	Appearance in Network Diagrams
Mentions	A user creates a Tweet containing another user’s name, indicated by the “@” character preceding the other user’s name.	A line from the user to another mentioned user.
Retweet	A user reposts or forwards a Tweet written by someone else.	A line from the user to another retweeted user.
Mentions in retweet	A user is mentioned in the original post when the other user retweeted the Tweet.	A line from the user to the mentioned user.
Replies to	A user responds to another user’s Tweet.	A line from the user to the user being replied to.
Tweet	A user posts an original message without any other user’s information.	A self-loop.

). This network visualization helped answer research question 2: How is scientific information contributed and distributed in this affinity space?

To answer research question 3 (How is the flow of information in the Twitter network influenced and controlled?), we used social network graph metrics. Specifically, centrality measures of influence (degree, betweenness, closeness, and eigenvector) were calculated in NodeXL (Smith et al., 2010). Degree centrality measures the number of connections a person has in the network (Hansen et al., 2020). Betweenness centrality helps identify individuals who play a “bridge spanning” role in a network (Hansen et al., 2020, p. 83). Closeness centrality shows that if information needs to flow through the network, a person may need a few or many steps to send messages to all other people (Hansen et al., 2020, p. 83). The eigenvector centrality network metric considers not just “how many people you know” but also “who you know” (Hansen et al., 2020, p. 84). These measures can identify key people in influential locations in the discussion network, highlighting the people leading the conversation. Additional network graph metrics include the numbers of edges and vertices, geodesic distance, and density, as defined in Hansen et al. (2020). Edges represent connections between entities. Entities are referred to as vertices or nodes. The geodesic distance is the length of the shortest path between two people in a network. It gives a sense of how “close” community members are to one another. The graph density is a number between 0 and 1 and is calculated from the number of actual connections in the network and the number of possible connections that are determined by the number of people in the network. It measures how interconnected people are in the network. Hansen et al. (2020) suggest larger social networks tend to have lower graph density. For additional information please see Supplementary Material.

5. Results

This affinity space contains 53,311 members and 136,126 connections (i.e., original tweets, replies, and retweets). Regarding categories, 45% were Scientist (n = 24,125), 32% were Public (n = 16,803), and 23% were Education and Outreach (n = 12,383). While Scientist remained the primary participants in scientific topics on Twitter, the Public and Education and Outreach users also shared in this space to communicate about science.

5.1. Social Network Analysis

Our analysis of the entire social network showed that the members and their interactions in this affinity space form Community Clusters (Figure 1). According to Smith et al. (2014), the Community Clusters structure is usually formed around some popular topics and may develop multiple smaller groups, which often form around a few hubs, each with its own audience, influencers, and sources of information. These Community Cluster conversations look like bazaars with multiple centers of activity (Smith et al., 2014). This structure creates a collection of medium-sized groups and a fair number of isolates.

Conversations surrounding the three hashtags of #ScienceTwitter, #SciComm, and #AcademicTwitter consisted of a total of 2240 groups, ranging from 2 to 6247 people. Most groups were medium-sized groups formed around a few central entities. There was also one group containing 3093 individuals, who were isolated, meaning that they did not communicate with anyone else. People built connections with others in groups in four main ways: mentions, retweets, mentions in retweets, and replies to (Table 3). Of the 136,126 connections created by 53,311 users, there were 59,941 mentions in retweets, 49,134 retweets, 17,237 mentions, 7976 tweets, and 1838 replies to (

Table 4. Overall network graph metrics.

Graph Metric	Value
Graph type	Directed
Vertices	53,311
Total edges	136,136
Number of edge types	5
Mentions	17,237
Tweets	7976
Retweets	49,134
Mentions in retweet	59,941
Replies to	1838
Self-loops	8210
Reciprocated vertex pair ratio	0.03613
Reciprocated edge ratio	0.06975
Connected components	4700
Single-vertex connected components	3093
Maximum vertices in a connected component	44,955
Maximum edges in a connected component	127,042
Maximum geodesic distance (diameter)	18
Average geodesic distance	4.42965
Graph density	0.00005
Modularity	0.71383
Groups	2240

). The distinct differences in numbers (i.e., 7976 tweets versus 59,941 mentions in retweet) means that most people in this network interact with others in different ways. Figure 2 shows two central users’ self-descriptions and categories, and how their tweets spread across the social network. Measuring how central users are reveals the influential users of this social network and how they are connected to one another.

In this network, the maximum geodesic distance (diameter) is 18 and the average geodesic distance is approximately equal to 4. This suggests that the farthest distance between two people from each other is not too far and the average length of the distance between all pairs of people is small. The graph density is 0.00005. In this affinity space, people form a less tightly connected community structure despite interacting and connecting with other members of the same group or other groups.

We selected the first 50 groups to display the new social network graph using NodeXL’s community detection algorithm, in which groups (i.e., clusters) that are connected to one another are mapped and displayed. The first 50 groups are those that represented larger clusters of entities (Figure 3). It is important to note that for this network, the vast majority of groups (2190) consisted of small groups (i.e., isolates or dyads). These isolates or dyadic groups indicate that most people in this social network did not interact with others or interacted little and were limited to a few people they knew.

Most of the 50 groups showed shapes of clusters, with a few broadcast networks and one isolated group (

Table 5. Social network structures.

Network Name	Graph
Community Clusters
Group 1
Group 2
Broadcast network
Group 29
Group 41
Isolated group
Group 4

). Group 1 (label G1) exhibited a shape centered on certain people, which indicates that the sources of information and the paths of dissemination were in the hands of Scientist and Education and Outreach users. Group 2 (label G2) showed a group formed by three categories of people in almost equal numbers, which suggests that the Public, Scientist, and Education and Outreach users were discussing scientific topics together. Some groups showed a broadcast network where members were usually connected only to the central user and not to each other. Groups 29 (label G29) and 41 (label G41) showed such a network. Members in these groups replied or retweeted the central member, suggesting that the central member had some influence. These two groups also demonstrated groups where almost only one category (Scientist, Education and Outreach, Public) existed. This indicates that within such groups, people only built connections with people who are close to them in terms of their identity. Group 4 (label G4), on the other hand, was an isolated group. It was made up of independent members of different categories who did not connect with any other groups or persons. This suggests that in this group people posted tweets discussing relevant topics but did not interact with anyone else.

5.2. User Analysis

People within groups interact, and those at the center of influence build bridges of communication externally, allowing different groups to connect and form this large social network. Measuring how central users were reveals influential users and their connections to one another. Influential users were ranked by the score of degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality. Category descriptions were also provided for all users.

5.2.1. Scientists and Educators Held the Sources of Information and Public Disseminated Information

Degree centrality measures the number of connections a user has in the network. In-degree centrality measures the connections others initiate with a user. Users with high in-degree centrality scores can be seen as the center of communication since others mention, reply to, or retweet their posts. Out-degree centrality counts the connections a user initiates with others. A high out-degree centrality score means a user tweets a lot about topics to gain attention by mentioning or replying to others. These two metrics demonstrate which members control the flow of information and the level of engagement among members.

All top ten users in this network of in-degree centrality were Scientist and Education and Outreach users (

Table 6. Top ten users by in-degree centrality.

Rank	Category	In-Degree Centrality	Out-Degree Centrality	Network Group
1	Education and Outreach	3417	73	G1
2	Scientist	2984	177	G1
3	Education and Outreach	2175	492	G1
4	Scientist	893	1	G8
5	Education and Outreach	783	1	G10
6	Scientist	702	33	G1
7	Scientist	610	4	G18
8	Scientist	577	12	G1
9	Education and Outreach	575	132	G1
10	Scientist	565	4	G17

). The highest in-degree centrality (3417) was an Education and Outreach user. This means that these users were the center of this affinity space and held the source of information. Their posts received attention and were shared. For example, Scientist 1 tweeted, “Data visualization in R? Here is a great cheat sheet for ggplot2 that hopefully can be useful. Please share #AcademicTwitter #phdchat #Bioinformatics (All accessible PDFs of R-provided cheat sheets can be downloaded here: [HYPERLINK])” and added two images showing a cheat sheet for the ggplot2 package in R, which is useful for data visualization. Another scientist tweeted, “As a postdoc, students sometimes ask me for advice on how they should communicate with their Prof/PI. Having sent and received 1000s of academic emails, here’s what I’ve learned about how to be effective in communication with senior faculty. #phdchat #AcademicTwitter.” A user who was classified into the category of Education and Outreach wrote, “Hey, just don’t forget to start! #AcademicTwitter #phdvoice #phdfriend (An image showing meme for Gru’s plan “come with a research idea, reduce it to a research question, present it to your supervisors, Start!”).” These examples indicate that central, important users in the network were those who shared information, memes, or solutions to problems. However, high in-degree users did not have high measures of out-degree. We postulate that this might demonstrate (a) a focus on others, (b) a focus on themselves, or (c) a user with many followers who has prestige and whose messages are interacted with by followers. In any case, the top ten users embody certain features of affinity spaces, namely, within this common shared space, different kinds of expertise (i.e., categories of users) and expertise (i.e., focusing on different topics) were included.

Six of the top ten users of out-degree centrality were Public, four of which were bots (

Table 7. Top ten users by out-degree centrality.

Rank	Category	In-Degree Centrality	Out-Degree Centrality	Network Group	Note
1	Public	2	4112	G5	bot
2	Education and Outreach	1	3631	G2
3	Public	29	3132	G2	bot
4	Scientist	55	1658	G3
5	Education and Outreach	35	1493	G3
6	Public	0	508	G3	bot
7	Education and Outreach	2175	492	G1
8	Public	14	480	G3
9	Public	13	476	G3	bot
10	Public	105	253	G14

), which we ascertained from examining the user biographies. Within the user biographies, these bot accounts indicated that they were bot accounts, such as one that wrote, “friendly bot advocates female empowerment particularly stem, ai & tech” and another that wrote, “[account name] retweets #radiomics tweets tweets random (recent) radiomics paper hour accounts that used automated features instead of posting spam messages.” They forwarded useful information, automatically generated content, or automatically replied to other users. The user with the highest out-degree centrality (4112) was a bot. These bot accounts automatically retweeted tweets with specific hashtags, such as #scicomm, #scipol, #scidip, and #sciart, resulting in high out-degree centrality. There was only one Education and Outreach user who had high in-degree and out-degree scores. This means that this user actively interacted with others while people paid attention to their posts.

5.2.2. Scientists and Educators Connected Other Users

Betweenness centrality helps identify individuals who play a “bridge-spanning” role in a network. In this affinity space, eight of the top ten users of betweenness centrality were composed of Scientist and Education and Outreach categories, and a few were Public (

Table 8. Top ten users by betweenness centrality.

Rank	Category	Betweenness Centrality	In-Degree Centrality	Out-Degree Centrality	Network Group	Note
1	Public	487,998,649.044	2	4112	G5	bot
2	Education and Outreach	380,847,553.172	1	3631	G2
3	Education and Outreach	361,239,568.951	3417	73	G1
4	Public	357,300,566.203	29	3132	G2	bot
5	Scientist	254,143,564.522	2984	177	G1
6	Education and Outreach	175,013,982.037	2175	492	G1
7	Education and Outreach	75,409,275.180	783	1	G10
8	Scientist	72,572,046.716	893	1	G8
9	Scientist	55,259,967.550	55	1658	G3
10	Scientist	50,486,043.866	610	4	G18

). The highest betweenness centrality user was from the category of Public, which was also a bot account that had the highest out-degree centrality. All ten users had high in-degree or out-degree centrality scores. This shows that Scientist and Education and Outreach users built connections throughout the social network by acting as a bridge to other users, which is a feature of affinity spaces in that dispersed knowledge is encouraged and that everyone can contribute in some way (producing information or consuming it).

5.2.3. Public and Education Users Spread Information

Closeness centrality shows how closely connected people are in the network. Users with the highest closeness centrality scores were mainly in the Education and Outreach category (

Table 9. Top ten users by closeness centrality.

Rank	Category	Closeness Centrality	In-Degree Centrality	Out-Degree Centrality	Network Group	Note
1	Public	0.336	2	4112	G5	bot
2	Public	0.328	29	3132	G2	bot
3	Education and Outreach	0.325	1	3631	G2
4	Education and Outreach	0.316	3417	73	G1
5	Scientist	0.310	2984	177	G1
6	Education and Outreach	0.304	2175	492	G1
7	Scientist	0.293	55	1658	G3
8	Education and Outreach	0.289	35	1493	G3
9	Education and Outreach	0.287	90	88	G1
10	Public	0.287	13	476	G3	bot

). This affinity space features a poor closeness centrality—0.336 was the highest observed—which indicates that users were relatively distant from each other throughout the entire Twitter network. Users with both a high closeness centrality and a high in-degree centrality were closer to others and could get information to others relatively quickly. Similarly, users with both a high closeness centrality and a high out-degree centrality could share others’ posts relatively quickly. This means that these users were the first to be considered when information needed to be effectively communicated and spread to most people in this affinity space. This is an example of the affinity space feature of different forms and routes to participation: Some people dispersed information while others received it.

5.2.4. Public and Education Users Had Many Influential Connections

Eigenvector centrality helps identify who is the most influential. The users with high eigenvector centrality were mostly those categorized as Public and Education and Outreach (

Table 10. Top ten users by eigenvector centrality.

Rank	Category	Eigenvector Centrality	In-Degree Centrality	Out-Degree Centrality	Network Group	Note
1	Public	0.401	2	4112	G5	bot
2	Public	0.331	29	3132	G2	bot
3	Education and Outreach	0.309	1	3631	G2
4	Scientist	0.168	55	1658	G3
5	Education and Outreach	0.152	35	1493	G3
6	Education and Outreach	0.130	3417	73	G1
7	Scientist	0.122	2984	177	G1
8	Education and Outreach	0.106	2175	492	G1
9	Public	0.079	14	480	G3
10	Public	0.072	13	476	G3	bot

). This indicates these users had many connections with others while being highly connected to some popular individuals. This might have impacted how information flowed in the affinity space. Information disseminated among these users was more effective relative to others because they were influential and the users they were connected with were equally influential.

Betweenness centrality, closeness centrality, and eigenvector centrality measures help identify who is important or central in this affinity space. They may be important bridge-builders to connect other different parts of the network, or they may be at the center of the network getting attention from or giving attention to other members. These key people are in influential positions and lead the conversations in this affinity space. Without them, messages would be difficult to send and share.

6. Discussion

6.1. Using Social Media Platforms Like Twitter as Tools for Accessing Science Information

The results of this study showed that 53,311 members of the affinity space of #ScienceTwitter, #SciComm, and #AcademicTwitter consisted of three categories: Scientist, Public, and Education and Outreach. All three categories were evenly represented rather than one category having a particularly large or small number of members. Scientists and the public remain an important part of the science-related conversation on Twitter (Bex et al., 2019; Moukarzel et al., 2020; Bhandoria et al., 2021). We found that users who identified as Education and Outreach also made up a significant portion (23%, n = 12,383). This validates that educators were using Twitter as a tool for accessing information, participating in their respective communities of interest, and sharing their insights on specific topics (Malik et al., 2019; Prestridge, 2019). With the scientific exodus from Twitter and the rise of other similar platforms such as Mastodon and Bluesky, we anticipate that such sites encourage users from varied backgrounds to access information, participate in communities, and share insights. Thus, we see potential for reproducing a study like ours on these newer platforms.

6.2. Social Networks Can Be Better Understood Through Indentifying the Users

We found that scientific information was shared among members and formed a social network with a Community Clusters structure. This shows that the affinity space of those who used the hashtags #ScienceTwitter, #SciComm, and #AcademicTwitter was shared regardless of expertise. This study also shows that social network analysis does not have to be limited to structural analysis (Smith et al., 2014); rather, social networks can be better understood by identifying users in the social world. This helps us understand the important and central members in the network—authoritative members who can quickly spread and share information. This is important, especially when specific information needs to be disseminated, and those members who are more connected in the network can help break down information barriers for the purpose of promoting or explaining science (Ahmed et al., 2020). Additionally, similar to many other studies on large social networks, some metrics, such as density, lose their explanatory power (e.g., Darmon et al., 2015). By triangulating other metrics, including centrality measures, we can make more informed decisions as to who is influential within this affinity space.

6.3. Bots Spread Information, but Some Bots Are Malicious and Difficult to Detect

Within this affinity space, Scientist, Public, and Education and Outreach members all had important information dissemination roles. Although the members with high centrality scores contained four bot users, according to their descriptions, they were all accounts that used automated features instead of posting spam messages. They forwarded useful information, automatically generated content, or automatically replied to other users. Their presence made information dissemination faster, and their outward activities (retweets) built more connections in this affinity space. Most research focuses on how to identify Twitter bots rather than discussing how to properly utilize bot accounts for science information campaigns (S. Lee & Kim, 2013; Minnich et al., 2017; Feng et al., 2021). In this study, we found that bots had high centrality scores in the network. In future research, it may be possible to explore how to use bot accounts to collect information, such as forwarding posts from specific accounts and extracting keywords to simplify information and then share the information to the public.

6.4. This Online, Social, Scientific World Fulfilled Certain Aspects of Affinity Spaces

This study demonstrated that the science-based affinity space on Twitter was composed of diverse users attracted to certain hashtags. There were many different forms and routes to participation (Gee, 2012). People participated on Twitter by creating posts, following hashtags, and interacting with others (e.g., retweeting, replying to, mentioning). In addition, this affinity space fulfilled certain features evidenced by the centrality measures, including sharing the space regardless of expertise, providing multiple routes to status, and having a porous leadership structure (Gee, 2012). Affinity spaces are not limited to physical space. With the current development of science and technology, digital space breaks geographical and time constraints, and more interactions and content sharing are taking place. Our research confirms this. In this affinity space, people from diverse backgrounds came together to pursue common endeavors for scientific communication. From scientists to educators to the public, people could be novices or experts, express their opinions, and connect with people they might have known (or not!). Our research promotes the study of affinity spaces by identifying the degree of features. While affinity spaces are conceptualized to have a high degree of interactions, the network density in this study is notably low (0.00005). An implication for improving science communication on similar platforms is to examine ways to improve interactions that occurred in the affinity space. We see similarities in our study to Staudt Willet’s (2019) and Neely and Marone’s (2016) studies with specific user groups in that certain features of the affinity space need to be strengthened. Any space that has more of these features is closer to being a paradigmatic affinity space (Gee, 2012). By improving affinity space degree, users are more engaged, have access to better resources, and can interact more meaningfully with each other. Consequently, they are likely to learn more and develop their skills and knowledge more effectively.

6.5. Limitations and Future Research

Since this study focused on analyzing who the users engaging with scientific topics on Twitter were and how they communicated, we paid less attention to the content of the tweets themselves. Future research, such as content analysis in which researchers classify tweets to learn which types of posts get the most attention and provide practitioners with strategies for writing tweets, would potentially provide new insights to this affinity space. In addition, the data was collected over a period of only one month, and it cannot be ruled out that a specific time or event will affect the density of people’s engagement in the topic. Future work could examine longitudinal studies of science communication, such as changes in how people discuss scientific topics on microblogging platforms over several months or years. These sorts of studies could investigate changes (if any!) that occur in users identified as Scientist, Public, and Education and Outreach. Research into designing affinity spaces that feature information or roles for these groups could also be fruitful. An additional line of research could entail examining the data from an Ego-net analysis in which entities and their connections are more deeply investigated to understand if homophily (i.e., like-minded individuals communicating with one another) played a role in the flow of communication within this affinity space (Moukarzel et al., 2020). We also acknowledge the highly quantitative nature of this work, which can limit in-depth understanding of the context and people whose data we collected and analyzed. Future work could bring deeper understanding through in-depth analysis of representative users’ accounts and social networks followed by interviews with such users.

7. Conclusions

The purpose of this study was to identify the users of the Twitter online world formed by three scientific hashtags. We found three roles: Scientist, Public, and Education and Outreach users. We presented the taxonomy we used, PIT, as a valid and reliable tool that is based on users’ self-identification (Twitter user bio) and how users primarily describe themselves in their scientific practice.

In this online world, all three categories of users were playing key roles, especially educators, who both connected others and spread information. This is important for educators, who may be effective communicators of scientific information without knowing it. In other studies that feature educators on Twitter (e.g., Richter et al., 2024; McNeil et al., 2024), educators learned from other educators. In our study, educators were able to share their knowledge with people outside their circles. This finding provides further evidence for the notion that social media sites such as Twitter allow for learning amongst different kinds of people (Demir, 2024). Our findings are also important for researchers, as they can use this study as a basis for determining how educators contribute to science communication on social media. Further research in this context is highly recommended and essential to promoting how science communication can appeal to the public and reduce misinformation.

Finally, our study enriches the application of affinity space theory. This study, like other studies on teachers (e.g., Marcelo-Martínez & Marcelo, 2025; Na & Staudt Willet, 2022) and chronically ill people (Sharma & Land, 2018), provides evidence that Twitter meets criteria for being a virtual affinity space. Additionally, this study proves that affinity space theory is also applicable to a wider range of science topics.