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Is the Backchannel Enabled? Using Twitter at Aca-
demic Conferences 11Paper presented at the 2011 Annual
Meeting of the American Educational
Research Association, New Orleans,
April 8–12,2011. Correspondence
should be addressed by email to
<bodong.chen@gmail.com>. APA
Style citation:
Chen, B. (2011, April). Is the
Backchannel Enabled? Using Twit-
ter in Academic Conferences. Paper
presented at the 2011 Annual Meeting
of the American Educational Research
Association (AERA), New Orleans,
Louisiana.
Bodong Chen
OISE/University of Toronto
Abstract: Twitter, a social network and micro-blogging service that
allows people to post small bursts of real-time information, is increas-
ingly adopted by academic conferences to enable a backchannel for
communication. This backchannel enriches communication at a con-
ference that is usually solely supported by face-to-face interactions.
To investigate what discourse Twitter could afford in the conference
setting and how participants contribute to academic discourse through
Twitter, in the present study I incorporated quantitative analysis, in-
formation visualization, and social network analysis techniques and
analyzed a corpus of tweets from seven academic conferences. Results
showed various types of Twitter usage at academic conferences as well
as four categories of participants. This study also revealed some inter-
esting phenomena of academic discourse on Twitter which might shed
light on future practice.
Keywords: Twitter, social media, academic conference
Introduction
After the first Web 2.0conference hosted by O’Reilly Media and
MediaLive (O’Reilly, 2007), the term “Web 2.0,” together with the
notion of “user-generated content,” quickly spread all over the globe
and transformed people’s understanding of the web. In the past few
years, numerous Web 2.0services were launched, covering a wide
spectrum of services including social networking websites, video-
sharing websites, wikis, blogs, social bookmarking, etc. In contrast
to traditional websites which regard users as passive information
retrievers, a Web 2.0service allows users to make contributions to
communal spaces and to communicate with each other easily. This
trend encourages a new “participatory culture” in the information
age (Jenkins, Clinton, Purushotma, Robinson, & Weigel, 2006).
Twitter,2created in 2006, is a social networking and micro-blogging 2Twitter, http://www.twitter.com and
http://en.wikipedia.org/wiki/Twitter.
service that enables users to send and read each other’s messages,
which are called tweets. It is amongst the most influential Web 2.0
services and is still expanding rapidly. Twitter is a simple and ag-
ile communication tool, restricting each tweet to no longer than 140
characters. As a social network service, Twitter allows users to estab-
lish and maintain connections by following each other’s tweets. When
you become a follower of a user, that user’s tweets will appear in a
timeline on your Twitter feed. Furthermore, Twitter provides four
is the backchannel enabled?using twitter at academic conferences 2
main ways for users to interact, including:
• RT: A user can share a tweet of another user by “retweeting” that
tweet. The new post usually follows the syntax of “RT @origi-
nal_user tweet_text_body”
• @reply or mention: An @reply is a tweet that begins with @user-
name which addresses the receiver of this message, while a men-
tion is a tweet that contains @username in its body. Either an
@reply or a mention is public and accordingly can be accessed by
other users
• Direct Message (DM): A direct message is a private corresponding
between only a sender and a recipient
• Hashtag: Users can group together tweets with mutual interests
or topics using hashtags—words or phrases prefixed with a pound
sign (i.e. “#”). It provides a way of aggregating tweets and enables
users to find people and communities with the same interests
Since Twitter puts comparatively more emphasis on individuals
rather than group or community, hashtagging becomes an extremely
important feature to bring people with a same interest together and
to nurture community. For a specific event such as a conference,
hashtagging is a simple way to aggregate scattered messages to a
shared “playground” and to build a channel for information ex-
change and communication.
Research on Using Twitter on Conferences
Regardless of Twitter’s popularity in various contexts, tweeting on
an academic conference is a relatively new phenomenon. In this pa-
per, an academic conference means a conference for researchers to
present and discuss their work.3In a conventional academic confer- 3Academic Conference - Wikipedia,
http://en.wikipedia.org/wiki/Academic_conference.
ence setting, the space is divided into a “front” area for the speaker
and a large “back” area for the audience (Ross, Terras, Warwick, &
Welsh, 2010). In this context, attention is solely focused at the front
and interaction is usually limited. This traditional conference model
has a variety of problems, including feedback lag, stress for asking
questions, and participation decrease caused by the “single speaker
paradigm” (Anderson, 2008; Ebner, Beham, Costa, & Reinhardt,
2009). Moreover, there is little chance for the audience to interact
with each other and to collectively construct understanding of given
speeches.
Current endeavors to address those problems usually fall into two
kinds of practice. The first kind of change is represented by a trend
is the backchannel enabled?using twitter at academic conferences 3
named as “unconference”.4An unconference does not follow the 4For more information, please refer to
http://www.unconference.net/ and
http://thatcamp.org/.
routine of organizing a traditional conference; it invites participants
to negotiate the content and structure of the conference according
to their own interests (Crossett, Kraus, & Lawson, 2009). The other
type of solutions tends to explore the possibilities of using digital
tools to build “backchannels,” which typically assume a secondary
or background channel for communication besides question and
comment sessions at conferences (Harry, Green, & Donath, 2009;
McCarthy et al., 2005).
As a best representative of social media, Twitter is more and more
commonly used in academic conferences, mainly thanks to its sim-
plicity, increasing ubiquity of wireless access, and the booming of
the mobile Internet. However, to date research about using Twitter in
academic conferences has been scarce. Previous studies have mainly
focused on three aspects. The first focuses on users and usages of
Twitter. For example, a few previous studies have investigated who
uses Twitter at conferences, and why and how they use it (DeVoe,
2010; Ebner et al., 2009; Ross et al., 2010). Based on a survey with a
small sample, Ebner and colleagues (2009) identify attendees, on-
line attendees, speakers and organizers of conferences as the main
user groups of Twitter at a conference. By conducting a content anal-
ysis on users’ tweets, Ross et al. (2010) have identified seven main
purposes of using Twitter during conferences: comments on pre-
sentations, sharing resources, discussions and conversations, jotting
down notes, establishing an online presence, and asking organiza-
tional questions. The second type of research focuses on interaction
on Twitter during conferences, for instance, what types of interaction
users are engaged in and whether Twitter encourages a participatory
culture (McNeill, 2009; Ross et al., 2010). The last cluster of research
centers on the effect of using Twitter for academic conferences. For
example, two studies explore whether the use of Twitter enhances
conference experience, collaboration, and collective building of
knowledge (McNeill, 2009; Ross et al., 2010). Additionally, Letierce
and colleagues (2010) incorporate timeline analysis and social net-
work analysis together to study whether the use of Twitter could
help reach a broader audience. To summarize, previous research on
using Twitter at academic conferences mainly relies on descriptive
statistical analysis on survey responses or on users’ tweet counts.
Although there are studies that incorporate content analysis and so-
cial network analysis, they fail to go any deeper by further analyzing
their visualizations to uncover underlying principles.
This paper presents a study that analyzes Twitter use in seven
different academic conferences which happened from 2009 to 2011.
By analyzing a rich set of tweets aggregated by particular hashtags,
is the backchannel enabled?using twitter at academic conferences 4
this paper investigates the characteristics of Twitter users, online
discourse, and social networks on Twitter during these conferences.
More specifically, this study wants to answer the following research
questions:
• What are the common user patterns of Twitter at academic confer-
ences?
• What are the dynamics of different types of discourse on Twitter,
i.e. retweet (RT) and @reply?
• What is the relationship between tweeting, being retweeted and
being responded? What are the different types of users?
Methods
Participants and dataset
Seven academic conferences that took place from 2009 to 2011 were
studied. They were selected mainly by convenience sampling, but
also with a consideration that they represent different research fields
and conference scales as much as possible. These conferences in-
clude: Medical Library Association’s 2009 Annual Meeting and Exhi-
bition (#MLA09); Coalition for Networked Information Spring 2010
Membership Meeting (#cni10s); ED-MEDIA 2010 - World Conference
on Educational Multimedia, Hypermedia & Telecommunications
(#edmedia); Console-ing Passion 2010 - International Conference
on Television, Audio, Video, New Media, and Feminism (#CUPO);
Chartered Institute of Library and Information Professionals’ New
Professionals Conference 2010 (#npc2010); Institute for Enabling
Geospatial Scholarship meeting (#geoinst); and International Confer-
ence on Learning Analytics and Knowledge 2011 (#lak11). Themes
of these conferences ran across extensive fields, including education,
digital humanities, library science, computer science, geology, and
information and media studies; their forms and scales also varied
from meeting among a small group of scholars in two days, to large
scale conferences with thousands of participants. During these con-
ferences, Twitter hashtags, e.g. #MLA09, were officially or unofficially
claimed and used by a number of people; this made it possible for
people to track a particular conference from a distance and to retrieve
tweet archives later as well.
Public tweets with specific hashtags for these conferences were
collected through Google Docs spreadsheets and Twapper Keeper.55Twapper Keeper is a tweet archiv-
ing service that could capture ev-
ery single tweet containing a hash-
tag once the archive is created,
http://twapperkeeper.com/.
The retrieved dataset is composed of 8073 tweets posted by 1221
distinct users.
is the backchannel enabled?using twitter at academic conferences 5
Data analysis
A descriptive statistics analysis was first conducted to provide ba-
sic information about Twitter use in each conference. This analysis
includes a number of variables, including number of Twitter partic-
ipants, total number of tweets, number of tweets per user, retweet
percentages, message percentages, etc. To better understand the dif-
ferent patterns of Twitter usage in these cases, a cluster analysis on
the seven conference cases was conducted.
To study the dynamics of Twitter discourse on these conferences,
a visualization tool that visualizes each user’s tweets in a chrono-
logical order was developed. This visualization tool also highlights
retweet and reply links between twitter users. In this way, we can
visually recognize discourse between Twitter participants, investi-
gate the dynamics of Twitter discourse, and discuss possible ways for
encouraging participation.
Finally, a Social Network Analysis (SNA) on every conference
Twitter community was conducted. Visual representations of social
networks were created with Gephi, a sophisticated software package
for SNA and various visualization tasks. This analysis, together with
a cluster analysis on all users in each community, could identify
different types of Twitter participants and reveal characteristics of
each type.
Findings and Discussion
Overview of Twitter Usage in all Cases
Table 1shows a summary of Twitter use in all seven conferences. As
illustrated by these important statistics, the situation of Twitter use
differs greatly among different cases. For example, in “geoinst” and
“CUPO” conferences, tweet counts per user are remarkably high,
while this score in some other cases is low. Similarly, difference on
percentage of retweets (RT) and messages (@reply) is also significant
and it may be an indicator of whether Twitter enables a backchannel.
To discriminate similarity and difference of Twitter usage in these
conferences, a cluster analysis has been done on the conference cases.
This analysis does not include variables related to conference scales,
such as total participants and total tweets, but only takes variables
relevant to actual Twitter usage, such as average tweet, retweet, and
public message counts, into account. The cluster analysis clearly
differentiates three groups:
• Group 1: CUPO and geoinst. According to Table 1, average tweet
per user in the two cases is very high, but with great variance
is the backchannel enabled?using twitter at academic conferences 6
Table 1: Summary of Twitter usage in all conferences
Users Tweets Retweets @Reply RT% Msg%
Sum M SD M SD M SD
All cases 1221 8073 6.61 20.26 1.16 5.13 0.7 2.83 17.55%10.62%
MLA09 383 2031 5.3 14.1 1.13 4.82 0.6 2.25 21.27%11.23%
cni10s94 477 5.07 12.76 0.64 2.5 0.2 0.63 12.58%3.98%
edmedia 226 687 3.04 6.69 0.85 5.66 0.43 2.95 28.38%14.12%
CUPO 80 963 12.04 26.72 0.86 1.79 1.34 4.45 7.17%11.11%
npc2010 117 795 6.79 17.04 0.73 3.2 0.5 1.01 10.69%7.30%
geoinst 106 1758 16.58 48.8 1.26 4 1.35 3.56 7.62%8.13%
lak11 215 1362 6.33 14.13 2.06 7.53 0.95 3.54 32.45%15.05%
among users (SD = 46.8and SD = 26.7respectively). Actually, the
most active Twitter users of the two conferences, posting 364 and
172 tweets respectively, take 20.7
• Group 2: MLA09, cni10s, npc2010, and edmedia. In these cases,
scores on average tweet, retweet, and message per user are close to
each other. However, the percentages of retweet and message in all
tweets vary greatly among cases, reflecting more subtle differences
in Twitter user patters on different conferences.
• Group 3: lak11. This case distinguishes itself from other cases by
high percentages of retweets and messages. Although the edmedia
conference also has considerably high percentages of retweets and
messages, average post number of its users is too small. So, it can
be said that Twitter use on the lak11conference is active and highly
interactive in comparison with other cases.
Although three different schemes of Twitter use are identified, the
Pareto principle (also known as the “80-20 rule,” see Koch (2011)) or
the Long Tail effect (Anderson, 2008) is approximately applicable to
all cases, i.e., 20% twitterers post 80% of total tweets (see Figure 1).
For Twitter use in academic conferences, the concept of legitimate
peripheral participation (Lave & Wenger, 1991) is the major form of
participation for most attendees. This fact is important in framing
our expectation and designing strategies in building the Twitter
backchannel for conferences.
Discourse Dynamics Enabled by Twitter
In order to go beyond descriptive analysis and more clearly illustrate
the dynamics of activities on Twitter, a visualization tool that visu-
alizes tweets in a chronological order was developed. Figure 2is an
example of visualization results. In the visualization, the horizontal
is the backchannel enabled?using twitter at academic conferences 7
Figure 1: Distribution of tweets per user
in the whole dataset
axis represents the timeline, increasing from left to right; the verti-
cal axis represents users sorted in a descending order of their tweet
counts. Each horizontal gridline stands for one single user, so we
can clearly see the participating process of each user. For these dots
and lines, one red dot stands for a tweet, a grey line means a retweet
connection, and a green line represents a message (@reply) between
two users. So from the visualization, we can easily tell who is tweet-
ing, when people tweet, who is retweeting from whom, and who is
talking to whom. Furthermore, we can also clearly see how tweets
scatter in a time span and among users, how well Twitter facilitates
and maintains discourse, and when the climaxes of Twitter use at a
conference happen.
One important characteristic of Twitter use at a conference is that
the virtual discourse highly parallels the physical progress. In a
zoom-in image of one conference (see Figure 3), with retweet and
message information filtered, simultaneous eruption of tweets from
time to time turns out to be concurrent with several favored keynote
speeches when the Twitter timeline is compared with the confer-
ence program. This characteristic is shared by other cases, and was
also mentioned in a former case study (Letierce, Passant, Breslin,
& Decker, 2010). Therefore, it is clear that Twitter was constantly
actively used by participants during the conferences. The correspon-
dence of physical events and tweets pikes is a solid piece of evidence
of Twitter’s function as backchannel in these academic conferences.
However, it also raises the question of how could we take advantage
of this correspondence to encourage more participation and informa-
tion spread and exchange.
According to the summary of descriptive statistics in the last sec-
tion and previous studies (Ross et al, 2010; DeVoe, 2010), retweets
is the backchannel enabled?using twitter at academic conferences 8
Figure 2: Timeline visualization of
#geoinst
Figure 3: A zoom-in visualization
image of #edmedia
is the backchannel enabled?using twitter at academic conferences 9
and messages take a considerable portion of total tweets. They serve
as the dominant communication mechanisms on Twitter. Colleagues
have argued about the importance of using “@” sign for “addres-
sivity” (Honey & Herring, 2009). Regarding the use of retweets and
messages, Figure 2reveals two interesting phenomena. Firstly, most
retweets stem from active users who post a lot (reflected by the image
that most grey lines start from the top). This might be explained by
the fact that while some people are actively tweeting about the con-
ference, most other attendees “in the long tale” are more inclined to
read and retweet tweets posted by “established” Twitter participants.
This links established between “elite twitterers” and “peripheral twit-
terers” show a shared attention among participants to tweet posts in
parallel with the physical conference track, and this shared attention
is vital for the success of the Twitter-enabled backchannel. Secondly,
retweeting usually happens more instantly than sending a message
(shown by the tendency that grey lines are overall steeper than green
lines). For this phenomenon, since messages or mentions show more
intimate connection than retweets, they more rely on existing social
networks, which make sending messages or mentioning more sus-
taining than retweeting. Moreover, shorter reaction time of retweets
might result from the fact that Twitter, as a real-time information net-
work, lacks a way to maintain important information at “the top” of
the discourse; as a result, late-comers would lose the chance to read
former quality posts because of the instantaneity of communication
on Twitter. For conference organizers, it would be of great value to
have important tweets accumulated for people who can only partici-
pate in part of the Twitter backchannel.
However, even though communication established by public mes-
sages could last longer, sustaining conversation is still scarce, as re-
vealed by the scarcity of “wave-like” green lines between two twitter-
ers. A previous study also reported a notable decrease of connection
in social network analysis when researchers raised the threshold of
directed replies from one to two (Letierce et al, 2010).
Social Networks and User Types
Although Social Network Analysis (SNA) seems to be a natural tool
to investigate Twitter, which is a social networking service, few pre-
vious studies has adopted this technique. This paper attempts to in-
vestigate social networks formed on each conference through Twitter.
Besides mathematics measurements, such as betweenness, centrality,
closeness, and cohesion, this paper focuses on identifying different
types of “actors” and their locations in social networks (Wasserman,
1994). In this manner, different types of Twitter users could be iden-
is the backchannel enabled?using twitter at academic conferences 10
tified. To triangulate findings from SNA, a cluster analysis on users
is also performed for each case. The cluster analysis considers three
variables, including user’s tweet number, times of being retweeted,
and received replies. By comparing results from SNA and cluster
analysis, we can better understand characteristics of each user cate-
gory.
By using prefuse and Gephi, I created social network visualiza-
tions for all studied conferences. Taking the visualization of #edme-
dia conference for example (see Figure 4), every single node in the
graph stands for a distinct user. For each node, its size presents the
number of tweets the user posted; the darkness of node color indi-
cates how many messages this user has received. This visualization
and its thumbnail (at the left bottom) present a major cluster of par-
ticipants in this community, as well as isolated small groups and
individual participants. From the visualization, we can identify con-
nectors, leaders, bridges, and isolates in the Twitter community and
summarize major user types.
In light of the visualization and cluster analysis results, users
could be roughly classified into four groups:
• “Engine participants”: This group is represented by user number
114,108,164 and 137 in this graph. They post a lot, attract atten-
tion from other people, get retweeted by others, and talk to differ-
ent people; they serve as “connectors” or “hubs” in this network.
These users are also clustered into the same groups in the cluster
analysis. Mapping to the physical world, these users are mostly
regarded as experts in the research domain of this conference and
get involved in a lot of offline conversations during the conference.
• “Pop stars”: This type of user is rare, with user number 70 (red
node on the left side) as the only representative in this case. This
type of user post a few tweets, but could stir up massive feed-
backs in the community. In this conference case, user 5served as
a keynote speaker whose speech won extensive acclaims. Cluster
analysis also successfully clusters this user into a separate group.
Each of other cases also contains one or two such users.
• “Lonely twitterers”: Some attendees could only get little attention
or feedback, even though they post a considerable number of
tweets. Number 85 to 49 belong to this category. One possible
reason they are relatively “lonely” is that they are not established
scholars and have little chance to be heard in physical world. In
addition, tweet quality of some users in this category is limited.
• “Peripheral players”: As discussed above, most participants’ par-
ticipation is peripheral. They post or retweet a few tweets; they
is the backchannel enabled?using twitter at academic conferences 11
may read a lot of tweets from other participants, but rarely engage
in conversations.
Figure 4: Social network analysis of
#edmedia
In this case of edmedia, conference organizers did not play an ac-
tive role on promoting the Twitter backchannel; they mainly used
Twitter as a way to broadcast notifications to participants rather than
stimulating discussions. However, in some other cases, such as lak11,
conference organizers were serving as engine participants by convey-
ing personal thoughts, posting important links, retweeting interesting
tweets from participants, and publicly addressing participants in
their tweets. Some conference organizers took advantage of their
large number of followers, and helped to get voices from some partic-
ipants heard by a broader audience by retweeting their posts. While
playing as active participants themselves is one way to promote the
backchannel for organizers, understanding roles of each type of par-
ticipant and looking for ways to meet their distinctive needs could
also encourage broader and more in-depth discourse on Twitter. This
direction calls for further research and experiments.
Conclusion
This paper analyzes a rich tweet dataset containing hashtags from
seven academic conferences. Based on a descriptive statistical anal-
ysis, I discussed common and different features of these Twitter use
is the backchannel enabled?using twitter at academic conferences 12
cases, and identified three distinctive types of practices of using Twit-
ter to establish a backchannel for academic conferences. To better un-
derstand the dynamics of online discourse on Twitter, I implemented
a visualization tool which visualizes tweets in a chronological and
networked manner. A closer look at the visualization reveals several
interesting phenomena about user interactions, including the instan-
taneity character of retweets in comparison with public messages;
this feature of retweeting could result in bury of important informa-
tion worth sharing to a broader. Therefore, finding an effective way
to keep important ideas alive in the Twitter discourse will be of great
value for conferences. Furthermore, a preliminary Social Network
Analysis and a cluster analysis on user’s data were conducted to
study the structure of formed social network and different roles of
participants. This analysis identifies four major types of users, which
include engine participants, pop stars, lonely twitterers, and periph-
eral players. A deep understanding on the roles and needs of each
category of users is crucial for conference organizers to nurture an
effective Twitter backchannel.
Future work will focus on ways to incorporate more advanced
visualization techniques to produce an interactive and usable visual-
ization tools and try to make them openly accessible for conference
use. Besides the links established by retweeting and mentioning
connections between users, semantic relationship would be another
promising dimension that could be used to link users or users and
resources. Also, a comparison between Twitter participants and
registered conference participants, together with discourse analy-
sis, would help to evaluate the coverage of Twitter use and the way
information spread within the Twitter community. These further di-
rections would provide more meaningful guidance for enabling the
Twitter backchannel for academic conferences.
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