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Book Title Social Computing and Social Media, Participation, User Experience, Consumer Experience, and
Applications of Social Computing
Series Title
Chapter Title An Exploration of a Social Media Community: The Case of #AcademicTwitter
Copyright Year 2020
Copyright HolderName Springer Nature Switzerland AG
Corresponding Author Family Name Gomez-Vasquez
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Given Name Lina
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Organization University of Tampa
Address 401 W Kennedy Blvd, Tampa, FL, 33606, USA
Email lgomezvasquez@ut.edu
ORCID http://orcid.org/0000-0002-9612-984X
Author Family Name Romero-Hall
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Given Name Enilda
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Organization University of Tampa
Address 401 W Kennedy Blvd, Tampa, FL, 33606, USA
Email eromerohall@ut.edu
ORCID http://orcid.org/0000-0001-5514-152X
Abstract Online professional communities on Twitter are increasingly gaining attention among users due to benefits
such as knowledge sharing, professional development, and relationship building. Millions of hashtags are
used every day in different disciplines (e.g., #educhat) or everyday situations (e.g. #MondayMotivation).
Hashtags have led to the creation of conversations about topics (e.g., #highered), serving as a point of
connection among different types of users. In the academic world, the hashtag #AcademicTwitter has
evolved into an online community of educators, graduate students, organizations, and others engaged in the
discussion of topics and issues related to academic life, funny moments, and survival stories. This paper
examines participants and communication patterns in the #AcademicTwitter community. Using content
analysis and social network analysis techniques, the researchers examined tweets including the
#AcademicTwitter hashtag to discover the community’s network properties, roles of the participants,
sentiment, and conversational themes. Findings indicated that the conversation was not centered on one
topic, instead several micro-communities were found. Top participants in the #AcademicTwitter
community were educators, media platforms, and other professionals which centered on conversations
related to topics such as accessibility, academic life experiences, and teaching and research support. The
study of social media in academic professional settings is still new. Our work contributes to the literature
of social networks in academia, helping better understand how users connect and the network that supports
the #AcademicTwitter community.
Keywords AcademicTwitter - Online professional communities - Teaching - Education - Social media
An Exploration of a Social Media Community:
The Case of #AcademicTwitter
Lina Gomez-Vasquez
(&)
and Enilda Romero-Hall
University of Tampa, 401 W Kennedy Blvd, Tampa, FL 33606, USA
{lgomezvasquez,eromerohall}@ut.edu
Abstract. Online professional communities on Twitter are increasingly gaining
attention among users due to benefits such as knowledge sharing, professional
development, and relationship building. Millions of hashtags are used every day
in different disciplines (e.g., #educhat) or everyday situations (e.g. #Mon-
dayMotivation). Hashtags have led to the creation of conversations about topics
(e.g., #highered), serving as a point of connection among different types of
users. In the academic world, the hashtag #AcademicTwitter has evolved into an
online community of educators, graduate students, organizations, and others
engaged in the discussion of topics and issues related to academic life, funny
moments, and survival stories. This paper examines participants and commu-
nication patterns in the #AcademicTwitter community. Using content analysis
and social network analysis techniques, the researchers examined tweets
including the #AcademicTwitter hashtag to discover the community’s network
properties, roles of the participants, sentiment, and conversational themes.
Findings indicated that the conversation was not centered on one topic, instead
several micro-communities were found. Top participants in the #Aca-
demicTwitter community were educators, media platforms, and other profes-
sionals which centered on conversations related to topics such as accessibility,
academic life experiences, and teaching and research support. The study of
social media in academic professional settings is still new. Our work contributes
to the literature of social networks in academia, helping better understand how
users connect and the network that supports the #AcademicTwitter community.
Keywords: AcademicTwitter Online professional communities Teaching
Education Social media
1 Introduction
The use of social media platforms in the education field are on the rise [1], encouraging
scholars to participate in online professional communities to enhance learning [2].
Twitter is used among academics, at all educational levels, as a teaching, learning, and
professional development tool [3]. Students, teachers, professors, and other profes-
sionals use Twitter as a pedagogical tool for enhancing learning environments that
promote engagement among users [1,4]. It also provides participants with opportu-
nities to collaborate, gather evidence, and reflect on their practice, with other professors
and professionals outside their institutions and fields [5].
©Springer Nature Switzerland AG 2020
G. Meiselwitz (Ed.): HCII 2020, LNCS 12195, pp. 1–12, 2020.
https://doi.org/10.1007/978-3-030-49576-3_38
Author Proof
About 1 in 40 scholars are using Twitter for scholarly chat and self-promotion, but
also for community building [6]. Educator-driven professional communities on Twitter,
such as #AcademicTwitter, #PhDchat, #AcWri, and #AcademicChatter are gaining
popularity among academics and students. Particularly, the hashtag #AcademicTwitter
has emerged in the past years as a prominent indexing tool where thousands of tweets
are sent every month. The hashtag #AcademicTwitter is used to share information,
provide support, and engage in conversations regarding the world of academia. Despite
the popularity of these social spaces, there is a lack of understanding of how users
interact with one another [7]. And while studies have addressed the importance of
professional communities and relationships for educators [8,9], it is yet unclear how
online professional communities shape these connections and relationships between
educators [10].
Our study contributes to the online professional communities’literature, by gaining
insights into the patterns of interactions in the #AcademicTwitter hashtag. The aim of
this investigation is to better understand the patterns of interactions of those using the
#AcademicTwitter hashtag professional community. The following research questions
guided this investigation:
RQ1: What are the demographics of the #AcademicTwitter users (i.e., gender, role,
and field)?
RQ2: What sentiments were expressed in the tweets tag with the #AcademicTwitter
hashtag?
RQ3: Who are the central participants in the #AcademicTwitter hashtag?
2 Literature Review
There is no denial that social media is now part of our daily lives. There is a range of
different social media platforms (i.e., Facebook, Instagram, Snapchat, YouTube, Lin-
kedIn, TikTok) and users are often actively participating in more than one particular
social media outlet. One of these social media platforms is Twitter. According to the
Pew Research Center [11], Twitter is used by 22% of adults in the United States. In
addition to linking with accounts that they wish to follow; Twitter users tend to connect
using hashtags. In Twitter, individuals use hashtags to reach a broader audience of
individuals who share a similar domain of interest. This is particularly the case of users
who seek to connect with a professional network of individuals. According to Romero-
Hall [12], professional growth via social media is generated through the social sharing
and refining of ideas in a network or community with a common domain.
Various researchers have explored the use of hashtags by different professional
communities in Twitter to better understand the social nature of interactions, types of
users, and content shared [13–16]. For example, Greenhalgh and Koehler [13] exam-
ined targeted and timely professional development after the terrorist attacks in Paris in
November 2015 by analyzing tweets with the hashtag #educattentats. This hashtag
served as a temporary affinity space to provide support for teachers preparing to address
the incident with their students. However, unlike other hashtags use for professional
communities in Twitter, this was a temporary space that was only used for 28 days.
2 L. Gomez-Vasquez and E. Romero-Hall
Author Proof
Rashid, McKechnnie and Gill [14] investigated advice that is given to newly qualified
doctors as they start their career via the hashtag #TipsForNewDocs. The results showed
that most tweets focused on professional development as well as knowledge sharing, of
both tacit and know-how knowledge. There were also humorous tweets related to
socialization. Gomez and Waters [17] explored a Twitter hashtag created by profes-
sionals in the Public Relations fields that served as a point of connection among
educators, practitioners, students, and various other organizations. An analysis of the
network properties and actor roles of the hashtag #PRProfs showed that conversations
in this Twitter community are predominantly about sharing knowledge, teaching tips,
and trends in the PR industry [17].
Another example of the exploration of hashtags to better understand a Twitter
professional community is the research conducted by Kimmons and Veletsianos [18]in
which the researchers collected tweets related the American of Educational Research
Association (AERA) Annual Meeting to better understand academic Twitter use dur-
ing, around, and between the annual conference both as a backchannel and general
means of participation. Tweets with the hashtags #AERA14 and #AERA15 were
collected and analyzed. The results served to compare participation patterns between
two years of the conferences. One major finding by Kimmons and Veletsianos [18] was
the difference in participation norms by students and professors.
There have been several investigations focused specifically on the use of the
#edchat hashtag and its users. For example, Coleman, Rice, and Wright [19] collected
post and survey teachers who used the #edchat hashtag to determine if the exchanges
between teachers served as continuing education and merited credit. The results indi-
cated that conversations between teachers using the #edchat hashtag were found to
generate social capital and bind a professional community. Staudt Willet [15] also
conducted an investigation focused on the #edchat hashtag. This author explored the
types of tweets that users contributed to #edchat and the purposes observable in the
tweets. The results indicated that based on the analysis of the #edchat tweets, posts
were mostly on topic related to education and the practice of teaching. Yet, Staudt
Willet [15] added that teachers were not using #edchat to its full potential, as tweets
were missing important emotional elements that tend to shape relationships and too
many times the hashtag was use more for self-promotion.
Researchers have address that the effective use of hashtags is determined by factors
other than its affordances and design such as the users’needs and desire, as well as
social, cultural, economic, and political environments [16]. In an investigation com-
paring three hashtags (#NutricionMOOC, #EdTechMOOC, and #PhDChat), Velet-
sianos [16] observed general participation patterns on these hashtags, the types of user
who contributed to the hashtags, and the content tags in the tweets with these hashtags.
The results of this particular study showed that there are a variety of outcomes on the
potential benefits of hashtags pending the contexts. Two of the hashtags in this
investigation (#NutricionMOOC and #EdTechMOOC) primarily served as mediums
for announcements and promotion rather than professional development and social
connection. Although hashtags offer significant opportunities for professional devel-
opment, teaching, and learning, they may or may not fulfill the need as expected [16].
An Exploration of a Social Media Community 3
Author Proof
The review of the literature gives some insights on how different hashtags in
Twitter, related to professional communities, have at times served to create networks,
ignite conversations, increase knowledge, and foster relationships. However, it is also
clear from the literature that at times hashtags for professional online communities
serve for shallow networks [16] or may provide temporary connections in a “just-in-
time”format [13]. The creation of quality interactions in any kind of setting requires
the investment of time, commitment, and the willingness to engage. Socialization,
networking, and the creation of connections are complex processes [14].
As stated by Coleman, Rice, and Wright [19]: “social capital brings members of a
group together in solidarity and coalesces a group with string bonds within a com-
munity.”Given the proper nurturing and attention, professional communities using
hashtags in Twitter can provide social capital to those engage. For academics, evidence
show that Twitter provides immediacy, reach, and scholarly engagement that is relax,
professional, and at time humorous [20,21]. In addition to the casual chatter, social
media used by academics has shown to provide a sense of belonging to a community,
cross country interactions, and additional learning resources and research collabora-
tions [22]. Due to the value that Twitter communities provide to academic discourse
and socialization, it is important to investigate different elements of this social media
network.
3 Methodology
3.1 Data Collection and Cleaning
This study uses quantitative content analysis and social network analysis techniques to
examine communication patterns and network properties of the #AcademicTwitter
community on Twitter. Netlytic [23], a free cloud-based text and social networks
analyzer developed by the Social Media Lab at Ryerson University in Toronto, was
used to recollect tweets that have the hashtag #AcademicTwitter. We ran Netlytic
software from March 1
st
to April 1
st
, 2019, to collect and analyze tweets. The raw
dataset has 26,287 unique tweets and 15700 unique users that participated in the
discussions.
3.2 Variables
For this investigation, we selected a random sample of 500 users to manually analyze
their Twitter bio profiles, following three variables:
1. Gender (individual, organization, or other). “Other”refers to a user who did not
provide a clear name, bio, or a photo that makes it harder to categorize as individual
or organization.
2. Field: STEM, Social Sciences, Arts & Humanities, Business, Education, Profes-
sional Studies, and others.
4 L. Gomez-Vasquez and E. Romero-Hall
Author Proof
3. Actor role: Originally 16 categories were identified: 1. Assistant Professor, 2.
Associate Professor, 3. Professor, 4. Other professors, 5. Researchers, 6. Teachers,
7. Graduate students, 8. Undergraduate students, 9. Professionals in education, 10.
Other professionals, 11. Individuals interested in education, 12. Universities/
colleges, 13. Nonprofits, 14. Journalists, 15. Media, 16. Others (do not disclose their
profession or job role; usually disclose hobbies or random quotes). After collecting
and examining the results, we merged some categories as shown in the findings
section.
The categorization was derived inductively, guided by grouping Twitter users by
their profession, position, and field. The actor role categories are mutually exclusive.
We selected the first role each participant disclosed in the bio for codification.
Sometimes users can indicate multiple roles, but researchers always coded the first role
or job they disclose. Two coders coded jointly the profiles and discussed to solve
coding discrepancies to assure reliability. Sentimental and textual analysis of 26,287
unique tweets were performed as well.
3.3 Social Network Analysis (SNA)
The name network (who mentions whom) was considered for analysis, which revealed
15,052 directional ties among 5560 nodes (posters with ties). We used two centrality
measures: in-degree and out-degree. In-degree centrality indicates the number of ties
(e.g., messages) a node (user) receives from others. A high in-degree centrality shows
the popularity of a user, which is actively mentioned by others. In contrast, a high out-
degree indicates an active participant who has the purpose to disseminate information
to the network. We used Netlytic software to calculate macro level measurements such
as density, diameter, reciprocity, centralization, and modularity to reveal network
structure.
4 Findings and Discussion
Researchers were interested in learning about the communication and network structure
of the #AcademicTwitter community by identifying influential actors. Descriptive
results are presented as follows: 82% of users were individuals, 11% organizations, and
7% others (i.e., researchers could not identify a categorization base on the bio). Table 1
indicates the most recurrent users, showing the involvement of professionals (profes-
sionals working in education or professionals interested in education), graduate stu-
dents, educators (Assistant, Associate, Professors, Lecturers), and researchers. Most of
the actors belonged to the STEM (31%), Social Sciences (23%), and Arts and
Humanities (13%) fields. Fourteen percent of the users did not disclose their field
(Fig. 1). AQ1
An Exploration of a Social Media Community 5
Author Proof
We found that most of the tweets were positive. A total of 4240 posts addressed
positive feelings such as great (1256), good (805), love (599), excited (384), and happy
(360). Only 584 tweets were negative, conveying feelings such bad (162), lonely (44),
tired (44), dull (38), and nervous (37). Figure 2shows the salient themes in the
#AcademicTwitter community indicating discussion topics concerned to graduate
students’life and overall success of professionals in academia. We also discovered a
call-to-action language that dominated the conversations such as give, make, check,
learn, and find.
The #AcademicTwitter community consisted of six main clusters with central
actors that influenced the way information traveled through the network, as shown in
Fig. 3. Clusters are a group of connected people which tend to communicate frequently
with others in the group and typically do not communicate with users outside of the
0%
5%
10%
15%
20%
25%
Fig. 1. Most predominant roles of the participants in #AcademicTwitter.
Fig. 2. Salient themes on #AcademicTwitter.
6 L. Gomez-Vasquez and E. Romero-Hall
Author Proof
cluster. The first cluster in the #AcademicTwitter community is influenced by a media
platform-social education content provider: @academicchatter (total degree: 447), the
second cluster is a media platform, The Chronicle of Higher Education: @chronicle
(total degree: 78), the third cluster, @ph_d_epression, another media platform, which at
the time of analysis-not during data recollection-, was inactive (total degree: 167), the
fourth cluster, another media platform: @humanbiojournal (total degree: 46), the fifth
cluster was a graduate student: @hannahlebovits (total degree: 85), and the sixth
cluster, the University of Guelph in Canada: @uofg (total degree: 149). All 6 clusters
are shown in Figure; isolates are also visualized. Each cluster has a different color.
Findings reveal that conversations were not centered on one specific and solid com-
munity, instead several micro communities were found.
Table 1indicates the top central participants by in-degree and out-degree centrality.
In-degree users are tagged in posts and tend to be popular. In this example, the Twitter
user @JuliaFtacek is tagged in the message with the @symbol: @JuliaFtacek I per-
sonally love #AcademicTwitter. I particularly enjoy connecting with other academics
inside and outside my field. I’ve had many a good discussion about methodology for
research and pedagogical methods for my classroom). Out-degree users post fre-
quently (either tweeting, retweeting or tagging users) and show good awareness of
others. For instance, RT @zra_research: Staying organized and time management are
half the battle in pushing research projects forward. This is a great example.An
Fig. 3. Main clusters in the #AcademicTwitter community.
An Exploration of a Social Media Community 7
Author Proof
interesting finding in the results is the active presence of @AcademicChatter as both an
in-degree and out-degree user as shown in Table 1. @AcademicChatter is a social
media education content provider for graduate students and academics.
The most recurrent posters (out-degree) were media outlets (e.g. @AcademicChatter
and @ThePhdStory), other professionals (@HigherEDPR, user is a communication
strategist for faculty and researchers), and professors in STEM disciplines (e.g.
@Carlymdunn_mph). In-degree users (users mentioned in tweets) were mainly media
outlets (@Phdforum, @AcademicChatter) and educational organizations such as
@TutorsIndia. It is also worth noting the minimal presence of Twitter accounts of
universities and colleges (only 15 profile users were found) in the conversations.
During the one-month period analyzed (March 1–April 1
st
, 2019), there was a peak
of tweets sent during March 21
st
as illustrated in Fig. 4. This was because several out-
degree users retweeted this tweet by RT @elizabethsiber: “#AcademicTwitter -did you
know that there’s free, reliable software that will close-caption your powerpoint
presentation?”Academics like to learn about free resources to improve our work,
especially during times of creating material that are more accessible for students.
Figure 4includes the number of posts over time ranging from as low as 480 tweets per
day to more than 1,500 tweets per day in the #AcademicTwitter community. An
average of 600–700 tweets were posted per day, making #AcademicTwitter an
important resource for academics.
Table 1. Central participants by different centrality measures.
Twitter handle Actor role In-degree Twitter handle Actor role In-degree
@uofg Nonprofit 149 @AcademicChatter Media 353
@Phdforum Media 136 @Carlymdunn_mph Educators 8
@AcademicChatter Media 94 @HigherEDPR Professionals 7
Fig. 4. Number of posts over time.
8 L. Gomez-Vasquez and E. Romero-Hall
Author Proof
The #AcademicTwitter network primarily specializes in information and resource
sharing but also users are constantly seeking for advice (e.g. Okay #academictwitter
How do I write a paper for a collected volume without sounding like it’smyfirst ever
undergraduate essay? For some reason, I seem to have totally lost my ability to, like,
use words that make me sound, like, super smart). The #AcademicTwitter community
involves users from a diverse set of professional backgrounds and fields, sharing
interesting resources information.
Social network analysis is a useful technique that reveals how information and
resources move through the network [24], identifying prominent actors in the network
[24,25]. The social network analysis performed to the #AcademicTwitter community
indicated the following measurements: Diameter (44), Density (0.000180), Reciprocity
(0.025680), Centralization (0.026280), and Modularity (0.923500). The #Aca-
demicTwitter community is a wide network with a few central participants dominating
the information flow, indicating a centralized network. Most users were disseminating
or retweeting information, but engagement was not always predominant between
network participants. Central actors in networked online communities are opinion
leaders who affect others and control the information flow [26], which usually is led by
a small group of dominant and engaged actors [27].
The first measurement examined was centralization (0.026280) which indicated that
there were few influencers in the network. If values are closer to 1 than 0, indicates that
a few central participants dominated the flow of information in the network. In addi-
tion, only 40% of the users tweeted more than one time, reafirming a centralized
network. Density is another network measurement which examines how close partic-
ipants are within a network. The #AcademicTwitter network had a density of 0.000180,
which indicates that mostly no one was connected to others in the network (values
closer to 1 are evidence of a close-knit community). Diameter calculates the longest
distance between two actors in a network, which in the #AcademicTwitter community
was 44, showing some actors with higher degrees of separation or connection and
presenting a wide network. Reciprocity (0.025680) indicates if users engaged in two-
way communication. Results showed that only 0.25% of the users participated in
conversations, showing a predominant one-way information network. Lastly, modu-
larity, determines if there are several small communities or a one singular community
in the network. A higher modularity (more than 0.5) indicates divisions between
communities as represented by clusters. The #AcademicTwitter network presented six
different clusters and a modularity value of 0.923500, indicating the presence of micro-
communities in this network.
Our results align with the work of Bruns and Burgess [28] and Gruzd, Wellman, and
Takhteyev [29] which indicate that people use hashtags to discuss topics of shared
interest. The #AcademicTwitter users participated (as in-degree or out-degree) by dif-
ferent reasons which can include connecting with other professors and professionals,
sharing resources, seeking advice, self-professional branding, or just having a break from
work. All actors involved in Twitter professional communities share a mutual aim which
is to distribute information that will potentially impact the communications flow [30].
Our #AcademicTwitter study also supports previous studies (Xu et al. 2015) which
found that information sharing and building relationships are the most important
aspects of Twitter conversations in online communities. #AcademicTwitter has
An Exploration of a Social Media Community 9
Author Proof
emerged during the past decade, building a community of educators interested in
making connections and relationships, providing support, and sharing resources about
teaching, research, service, and overall the academic life. However, there is still par-
ticipation inequality in social media where 90% of social media users are “lurkers”who
do not contribute to the communities [31]. Future studies could examine lurkers in
professional communities such as #AcademicTwitter, which could help understand the
motivations of their passiveness on social media platforms. In the same line, further
studies could answer what motivates academics to engage in learning and professional
communities and the benefits they receive. Previous studies [5] have found that online
professional communities are a source of continuous professional development for
academics, providing authentic and personalized opportunities for learning and
support.
5 Conclusions
Our study used Twitter to analyze online professional communities related to academia
and how participants were using Twitter and specifically #AcademicTwitter to support
their professional development and learning. Our paper also examined communication
patterns and user influence in the #AcademicTwitter community.
Social media platforms and online professional communities provide great
opportunities for learning, guidance, and academic support. People from diverse
backgrounds are turning to social interactions (e.g. online chat groups, discussion lists)
to satisfy their needs no matter if they are personal or professional [5]. Our paper
contributes to the literature of social media and education, providing insights in the user
role and influence in the #AcademicTwitter professional online community. #Aca-
demicTwitter is a growing and popular educational-driven community on Twitter that
could attract other active users in the discussion (e.g. nonprofits or industries), to
provide helpful information and opportunities to connect with scholars.
This study has some limitations that suggest avenues for further work. This is an
exploratory study in the use of Twitter among educational communities. Future studies
can take a more in-depth analysis to understand specific topics and content shared
among academics in online professional communities. The current study does not
involve the codification and further examinations of tweets (messages). Having few
influential participants and a network characterized by low two-way conversations,
does not mean that real collaboration wasn’t involved. In other words, further studies
could analyze the collaborative nature of the conversations, and even message purpose
(e.g. informative, educative, engagement, mobilization). This work has implications in
the education field as it identifies the prominent users in the discussion of educational
topics and issues on Twitter. Identifying these influential actors helps to provides
opportunities for academics to engage with these prominent users for networking,
support and collaborative opportunities.
10 L. Gomez-Vasquez and E. Romero-Hall
Author Proof
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