Microblogging in higher education: Digital Natives, knowledge creation, social engineering, and intelligence analysis of educational tweets

Article
Microblogging in higher
education: Digital Natives,
knowledge creation, social
engineering, and intelligence
analysis of educational tweets
Simon Cleveland
Nova Southeastern University, USA
Barcus C Jackson
Barnes-Jewish College, USA
Maurice Dawson
University of Missouri – St Louis, USA
Abstract
With the rise of Web 2.0, microblogging has become a widely accepted phenomenon for sharing
information. Moreover, the Twitter platform has become the tool of choice for universities
looking to increase their digital footprint. However, scant research addresses the viability of
microblogging as a tool to facilitate knowledge creation practices among higher education stu-
dents. This paper proposes a model to explain how students, as digital natives, leverage the
features of the Twitter microblogging for the transfer of knowledge. Finally, the paper examines
the dark side of Twitter as a privacy-leaking platform and issues a call to higher institutions for
specific security policies to prevent nefarious use.
Keywords
Digital Natives, intelligence analysis, knowledge creation, microblogging, social engineering,
Twitter
Introduction
The goal of university programs is to prepare students to face the challenges in the car-
eers of their choice through acquisition of knowledge. Such knowledge includes both hard
Corresponding author:
Simon Cleveland, Nova Southeastern University Carl DeSantis Building, 4th Floor Fort Lauderdale, FL 33314-7796, USA.
Email: sc1674@nova.edu
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skills – specific training of tools, techniques and methods used in organizations, and soft
skills – team building, problem solving, and leadership. With the proliferation of Web 2.0
tools and learning management systems, universities rely on a plethora of information and
communication technologies to facilitate the knowledge sharing process.
This paper explores the microblogging phenomenon and analyzes its potential for know-
ledge creation by leveraging the information foraging, media specificity, and knowledge
creation theories. The paper proposes a model to explain how the features of the Twitter
microblogging platform enhance students’ conveyance and convergence practices for the
purposes of acquiring and transferring tacit and explicit knowledge.
Moreover, the concept of Digital Natives is examined and the Twitter mobile application
is proposed as a tool for teaching and educating the Digital Natives through sharing of best
practices as identified in the Community of Inquiry theory.
Finally, the paper provides evidence of the potentiality for extraction of specific identifi-
able information from Twitter posts through the use of various open source tools. The paper
calls for the creation of specific policies to ensure that security and privacy of students’
educational tweets are protected.
Literature review
Educational knowledge creation process
The goal of university programs is to prepare students to face the challenges in the careers of
their choice through acquisition of knowledge. Such knowledge includes both hard skills –
specific training of tools, techniques and methods used in organizations, and soft skills –
team building, problem solving, and leadership. Nonaka (1994) categorized the knowledge
that develops hard skills as explicit, since this particular type of knowledge can be captured
in a written form and can be easily stored in systems. In contrast, knowledge that develops
soft skills is considered tacit, since it is difficult to explicate and is ingrained in action.
To explain the educational knowledge creation process, we employ the knowledge cre-
ation theory proposed by Nonaka and Takeuchi (1995), who depicted the knowledge cre-
ation as a spiral process that winds through the various knowledge patterns (Figure 1).
Figure 1. Nonaka and Takeuchi’s knowledge creation model (1995).
2 E-Learning and Digital Media 0(0)

Socialization and personal notes. The theory proposes that the creation of knowledge occurs
through the exchange of information through four patterns. The first pattern is known as
socialization. Here, students engage in the transformation of tacit into tacit knowledge. This
occurs through observation, interaction, and in some instances imitation of the professor.
Moreover, the information exchange occurs through rich communication (e.g. face-to-face
or through synchronous medium) in an effort to develop shared mental models.
One useful tool to capture and reflect on shared mental models is through the use of
personal notes. Personal notes have been shown as information storage methods to portray
solution of problems, as archival tools to refresh the memory, and as information sharing
tools to encourage interaction among individuals (Koskinen and Aramo-Immonen, 2008).
For example, the Read, Encode, Annotate, and Ponder note-taking technique has been
shown to increase learning success and to facilitate common understanding among individ-
uals (Tasdemir, 2010).
Externalization and stories. The next pattern of knowledge creation is called externalization.
Here, instructors use metaphors and analogies to portray knowledge elements such as the
know-how, know-why, and care-why. At the same time, students transfer the shared tacit
into explicit knowledge via the use of written documents.
One tool that has been proven to facilitate learning through the externalization process is
the story. The story is essential in the recombination of personal and shared knowledge
through the process of collective- and self-reflection. Stories are textual representations of
lessons interwoven into a series of events and constrained by specific timelines. A story is used
to portray specific identity, facilitate a decision making process, and boost a social bond
between the storyteller and the listeners. For example, Jonassen and Hernandez-Serrano
(2002) found that stories can serve as problem-solving tools that reinforced the retrieval of
past experiences and used the lessons to address new problems. Furthermore, learning from
stories has been demonstrated to facilitate knowledge transfer among teams, since teams
frequently engage in the exchange of metaphors and storytelling to share lessons (Goffin
and Koners, 2011; Newell, 2004; Whyte and Classen, 2012). Through the art of storytelling,
students are able to catalogue past lessons and convert them into new mental models.
Combination and reflection. The third pattern is known as combination. Here, students trans-
form explicit into explicit knowledge through categorization and sorting of written and
digital documents, best practices, and methodologies. One tool to facilitate this process is
through personal reflection. Extant literature has demonstrated that reflection enables learn-
ing through the acquisition of lessons, promotion of innovation, capture of best practices,
and creation of soft skills (Knipfer et al., 2013; Loo, 2002; Matturro and Silva, 2010).
Internalization and lessons learned. Finally, during the internalization pattern, students convert
explicit to tacit knowledge through the process of collective reflection by joining the acquired
knowledge to validate and augment their individual mental models.
One method to facilitate this process is the collective assessment of lessons learned, typ-
ically performed at the completion of a course. The use of lessons learned is widespread in
the project management domain, but its applicability is universal to other domains as well.
In higher education, lessons learned documents can serve as valuable knowledge tools to
foster conversion of tacit into explicit knowledge, increase competency, and improve learn-
ing strategy (Cleveland, 2013; Williams, 2008).
Cleveland et al. 3

Student knowledge seeking behavior
The knowledge seeking behavior involves an active searching of information in order to
satisfy a specific information need (Xu et al., 2006). Students engage in knowledge seeking as
a result of gaps that exist in their mental models. Such gaps represent problems that require
specific courses of action (Pirolli and Card, 1999). To explain the knowledge seeking behav-
ior, Pirolli and Card (1999) developed the information foraging theory, in which they argued
that missing knowledge can be regarded as prey that is hidden in various sources (e.g. people,
systems, archives). In an effort to achieve the maximum value of information relevant to
their knowledge gap, information foragers will engage in knowledge seeking of the most
valuable information that will take the least amount of time and effort to locate. To apply
the theory to the education setting, it can be argued that a student who is seeking specific text
that describes a concept discussed in the class will seek to spend the least amount of time to
locate the source while dedicating more time to converting the knowledge from its explicit
into its tacit state. As a result, information located in hard to locate sources (e.g. file drawers,
archives, etc.) is expected to take a longer time and more energy to locate versus information
stored in online databases. Students forced to make decisions whether to hunt for hard-to-
locate information will focus on locating information which will require the least amount of
time and effort to locate.
To explain student knowledge seeking behavior, we apply the knowledge seeking process
model proposed by Savolainen (2006) (Figure 2). We propose that students’ knowledge
seeking behavior is triggered by the existence of a specific gap in their knowledge in relation
to proposed concepts in the classroom. Next, the students consider potential sources of
information and evaluate the ease of locating the most valuable information for the least
amount of effort. Upon locating specific information, students engage in the evaluation and
interpretation of the information. The process involves the weighing of whether 1) the infor-
mation successfully addresses the knowledge gap, or 2) further information is necessary to
bridge the gap. Based on the analysis, the seeking behavior may be repeated by students,
where additional information is sought for the same channel. The behavior will be termi-
nated if the discovered knowledge satisfies the knowledge gap.
Knowledge dimensions and interactions
Extant literature proposes that knowledge consists of four dimensions: situational, concep-
tual, procedural, and strategic (De Jong and Ferguson-Hessler, 1996). The situational
Identify new
knowledge gap
Consider knowledge
sources
Evaluate knowledge
sources
Locate knowledge
Evaluate if knowledge
satisfies existing gap
No
Yes
Figure 2. Students’ knowledge seeking process model (adapted from Savolainen (2006)).

dimension contains information associated with specific conditions. Such knowledge is
mostly superficial in nature and contains explicit properties. Its structure contains awareness
of an applicable model to resolve the problem of a specific case, is already automated (e.g.
step-based process), lends itself to verbal explication, and contains general properties
acquired through formal training.
The conceptual knowledge is factual in nature and is based on specific principles within a
domain. This knowledge is relational, structured through symbols, independent concepts,
and has verbalizable properties, but lends itself to transfer through diagrams, models, and
pictures due to its tacit nature. The procedural knowledge dimension is action oriented
within a specific domain. This knowledge comprises rules related to specific principles, can
be explicated through step-by-step processes or figures, and is domain specific.
Finally, the strategic knowledge dimension requires a deeper cognitive understanding of
specific concepts and abstractions, because it consists of unique plans and actions. Such
knowledge is transferred through demonstrations, pictures, and graphs, and requires deep
analysis.
To understand how technology can facilitate the transfer of each knowledge dimension,
we leverage the media synchronicity theory (MST). According to the MST (Dennis et al.,
2008), communication is essential for the seeking and acquisition of information. Moreover,
the communication process between individuals consists of numerous interactions, through a
variety of communication mediums, in order to satisfy the information gap. The theory
posited that these interactions come in two flavors: 1) conveyance – where individuals
share ‘‘new’’ knowledge to create new mental models, and 2) convergence – the sharing of
‘‘known’’ knowledge to validate existing mental models.
Dennis et al. (2008) argued that in the instances where new mental models need to be
created, the exchange of knowledge between the knowledge seeker and knowledge provider
needs to occur through a rich communication medium. In such instances, the parties need to
be highly in-sync with one another for longer periods of time, so that the seeker of know-
ledge can soak up large volumes of transferred knowledge from the knowledge provider. As
a result, the media synchronicity theory proposes that the conveyance process necessitates
communication channels characterized by capabilities for high synchronicity between the
parties (e.g. video/audio interactions). As a result, it is argued that such technology mediums
are more suitable for the transfer of the conceptual and strategic knowledge dimensions.
To facilitate the convergence process for the purposes of validation of existing mental
models, Dennis et al. (2008) argued that frequent interactions with relatively small amounts
of information exchange are required between individuals. For this purpose, technology
mediums that foster low synchronicity are necessary (e.g. short textual messages, web
links), and they are most suitable for the transfer of situational and procedural knowledge
dimensions.
These interactions are best illustrated by the process model shown in Figure 3, adapted
from Cleveland et al. (2015).
Microblogging
Universities are investing in numerous information and communication technologies in
order to facilitate the educational knowledge creation process. By some estimates, the learn-
ing management systems (LMSs) market is expected to reach US$8 billion by 2018
(McIntosh, 2014). Research demonstrates that such technologies positively influence the
Cleveland et al. 5

processes of socialization, externalization, combination, and internalization (Collins et al.,
2015; Lopez-Nicolas and Soto-Acosta, 2010). While many of these robust LMSs offer a
variety of rich communication channels for students to interact, the use of microblogging
for search and dissemination of knowledge is still rather scant.
Microblogging has been demonstrated to facilitate the distribution of knowledge practices
(Hanseth and Lyytinen, 2004; Seebach, 2012). It provides users with the ability to post short
messages through a variety of devices (Java et al., 2007). Users set up personal profiles,
subscribe and follow other users, interact with them through brief discussions, and share
web links, videos, and photos with their followers (Java et al., 2007). One of the most
popular microblogging platforms today is Twitter, which has over 320 million registered
users and one billion unique visits monthly to sites embedded in the Twitter message
(Twitter.com, 2016).
Presently, Twitter limits users’ posts to 140 characters. Extant studies show that this word
limit is sufficient for users to foster reflective information sharing in order to facilitate col-
laboration (Cleveland and Ellis, 2013; Yang and Chang, 2011). Moreover, studies have
demonstrated that, in some instances, the microblogging interaction supports the exchange
of knowledge and the development of learning (Ebner and Mauerer, 2008; Ebner et al.,
2010). As a result, it is argued that through its features, microblogging can aid users in
the process of educational knowledge creation.
In this paper, we propose the knowledge creation process illustrated in Figure 4 to explain
the conversion of information into learning through the various knowledge patterns.
Microblogging convergence through lessons learned and reflection. Communication channels such as
emails or discussion boards allow users the ability to develop their ideas without word limit.
In contrast, the microblogging platforms typically constrain each post to a predefined char-
acter length, where users are forced to exercise creativity in constructing concise posts.
Moreover, posted content is broadened since numerous users contribute to the same con-
versation topic. The interactions between users occurs through frequent exchanges of mes-
sages that contain position statements, questions, and specific answers. The exchanges can be
Figure 3. Knowledge dimensions and interaction model (adapted from Cleveland et al. (2015)).

one-to-one, where two users communicate with one another; one-to-many, where one user
converses with either two or more participants at the same time; and many-to-many, where a
group of users communicate and collaborate on a specific topic (Cleveland et al., 2015).
Twitter provides users with the ability to re-post (retweet) other users’ posts in their own
network. As a result, this exchange propagates knowledge exchange with new audiences. For
example, in their study, Zhang et al. (2010) found that users relied on microblogging to share
different types of information through frequent and lengthy conversations. The shared
knowledge assisted the participants with clarification on the type of work performed by
others and location of knowledge for the purposes of solving specific situational questions.
In their microblogging study, Zhao et al. (2011) found that over 90% of the posts were
work-relevant, lending further proof to the argument that microblogging facilitates the
transfer of situational and procedural knowledge. For example, nearly half of the informa-
tion exchanges (44%) concerned task statuses. The researchers determined that microblog-
ging served as a tool to exchange information related to daily work activities. In a similar
study, Riemer et al. (2011) found that microblogging facilitated transfer of knowledge
related to task coordination, delegation of specific work, work-related questions, meeting
coordination, meeting agendas, statements of opinions and feedback on ideas, and replies to
messages.
Studies demonstrate that microblogging has been used to facilitate user interaction for the
purpose of sharing lessons. For example, in their study on microblogging in an educational
setting, Ebner et al. (2010) showed that the platform is utilized for the capture of lesson-
related communication, by allowing one user to become part of another user’s learning
process. The platform was shown to enable collective user participation for the purposes
of specific problem solving.
In a related study, Carpenter (2014) found that nearly half of microblogging student use
was for the purposes of distributing links to education sources and related articles (24% of
peer-interaction tweets) and curriculum materials and lesson plans (23%). Moreover, Mu¨ ller
and Stocker (2011) demonstrated that microblogging helped users to acquire best practices,
access standards/quality procedures, and use and improve documentation standards, docu-
ment quality, and document processes. The researchers also discovered that the platform led
to an improved information flow, promotion of learning and knowledge management prac-
tices and learning, and diffusion of rich experiences, leading to more innovative thinking.
Figure 4. Microblogging knowledge creation process.
Cleveland et al. 7

Finally, Wright (2010) found that microblogging posts made by students were used for the
purposes of reflective thinking. Users reported that the platform helped them overcome
social isolation, while the character limit focused their attention on purposeful reflection
with regard to their experiences.
Microblogging conveyance through stories and personal notes. Earlier, we argued that personal
stories and notes are useful tools for the conversion of tacit into tacit and tacit into explicit
knowledge. Here we demonstrate the ability of the microblogging features to facilitate the
use of such tools.
Microblogging platforms provide users with the ability to group microblogging posts into
stories. These events are called social curation and deliver considerable value to the micro-
blogging community. Web services sites such as Storify and Togetter allow content organ-
izers to formulate a story from a list of microblogging posts. For example, Duh et al. (2012)
demonstrated that a sample of 10.2 million microblogging posts from 800,000 users con-
tained 96,000 lists consisting of diaries (9%), problem solving (16%), collections of long
articles (19%), and conversations on specific topics (19%). This multiplicity of interactions
demonstrates that users are able to transcend the character limitation and engage in uniform
storytelling for the purposes of information exchange.
Furthermore, Reinhardt et al. (2009) found that over 40% of users in their Twitter study
used the platform to capture personal notes for the purposes of knowledge building and
information sharing with others. Similarly, in their study of microblogging use, Pepe and
Mayernik (2012) demonstrated that the platform is used by scientists to document field
research data in the form of notes. Their results demonstrated that microblogging notes
taken on the spot as short text messages can facilitate the transfer of tacit into explicit
knowledge.
Microblogging and Digital Natives
The birthdate for Digital Natives is 1980. Digital Natives are children who have grown up
with technology (Prensky, 2001). Others have referred to Digital Natives as ‘‘Gen C, Gen I,
Net Gen, Gen Y, Gen Z, and Internet Generation’’ (Howell, 2012). Another name for this
generation is ‘‘Net Generation’’ or ‘‘Net Geners’’ (Tapscott, 1998). Tapscott (2008) back-
dated the ‘‘Net Generation’’ or ‘‘Net Geners’’ date of birth to 1977. Jukes et al. (2010) refer
to these Digital Natives simply as the ‘‘Digital Generation.’’ For the purpose of this paper,
Digital Natives is used.
Because these students have grown up in the Information Age, it is believed that these
students learn and think differently from non-Digital Native students. Despite this notion, a
large number of faculty members are using traditional teaching methods. It was found that
90% of students own laptops and 86% own Smart Phones. Use of mobile devices may be
banned in the classroom (Dahlstrom et al., 2013).
Digital Natives and Digital Immigrants were initially introduced to distinguish the dif-
ference between young people today and their elders (Prensky, 2001). As time passed, it was
believed that the idea of Digital Natives and Digital Immigrants would become less relevant
because people would have grown up in the era of digital technology. To that end, Prensky
(2009) introduced a new term, Digital Wisdom, and defined it as a twofold concept. He
referred to Digital Wisdom as the use of technology to access cognitive power beyond our
innate capacity and as a means to enhance our capabilities.

In contrast to mobile devices being banned, learning with mobile devices has been proven
to further engage Digital Natives in how they want to learn (Gikas and Grant, 2013). The
cost of providing mobile functionality for the LMS could be a barrier to this type of learn-
ing. The mobile module is not a part of the baseline LMS provided by the vendor.
Dahlstrom et al. (2013) demonstrated that students learn best when the learning envir-
onment is blended. While most faculty members are knowledgeable about traditional teach-
ing and learning approaches, he or she may need to learn best practices in teaching for online
learning to create an environment the students learn best in (Prensky, 2001). A widely
used theory for design and delivery of online courses is the Community of Inquiry (CoI)
theory. The need for the theory was driven by the need to understand computer-mediated
communication and computer conferencing in supporting an education experience (Garrison
et al., 1999).
While Twitter’s intended use is as social media, Twitter can be used as a tool for teaching
and learning in an educational environment. Twitter’s features and functions allow for
Digital Natives to learn in the way they want to learn using technology, in a blended learning
environment, and on a mobile device. Twitter also allows for faculty to put in practice online
teaching and learning best practices identified in the CoI theory. Twitter’s mobile app can be
used as an alternative to purchasing LMS vendors’ mobile modules to offset the cost.
As one can see, using Twitter in education can yield multiple benefits in teaching today’s
students; therefore, the purpose of this section is to further discuss why Twitter is a legit-
imate tool for teaching and learning. As mentioned above, Digital Natives learn best in a
blended learning environment; therefore, first Twitter and CoI will be discussed.
Twitter and CoI. The idea behind the CoI is computer-mediated communication and computer
conferencing in supporting an education experience (Garrison et al., 1999). The CoI con-
structs are: teacher presence, cognitive presences, and social presences. Cognitive presence is
the extent to which participants in any particular configuration of a community of inquiry
are able to construct meaning through sustained communication (Garrison et al., 2001).
Social presence is the ability of learners to project their personal characteristics into the
community of inquiry, thereby presenting themselves as ‘‘real people’’ (Rourke et al., 2007).
Teaching presence is defined as the design, facilitation, and direction of cognitive and social
processes for the purpose of realizing personally meaningful and educational worthwhile
learning outcomes (Anderson et al., 2001). The overarching idea behind the CoI is that the
three constructs must be present in the learning space in order for an education transaction
to take place.
Twitter allows for the CoI to be put into practice because faculty has the ability to allow
students to follow them. Once students are following the faculty member, teacher presences
can be addressed by the faculty member tweeting. Tweeting is posting content of no more
than 140 characters that followers can see. Content can be text, pictures, and videos. For
example, faculty could post a video or link to an article related to the learning objectives that
the faculty member is teaching about. The faculty can also facilitate social and cognitive
presences by making sure students are tweeting with one another in a timely manner and by
making sure the students’ tweets are contributing to the learning process.
Twitter allows for faculty to address social presence, because students can post content,
like pictures and videos, that will project him or her as real. Students are also able to follow
their peers and instructor and reply to tweets. Tweeting is important to the social presence
construct because it allows a conversation to happen with real people.
Cleveland et al. 9

Cognitive presence can be addressed using Twitter by students exchanging information
related to the learning objective. Students can add new knowledge that may not be a part of
the discussion. Students are also able to get immediate feedback from peers if the instructor
is not available to respond.
The fact that Digital Natives like learning in a blended learning environment and faculty
are able to address Digital Natives’ learning style using Twitter by putting CoI online
learning best practices in practice, Twitter is appropriate for teaching and learning in
education.
In addition to addressing the needs of Digital Natives, Digital Wisdom is addressed when
Twitter and CoI are married together. Twitter is the technology that houses the information
that allows users to become wiser because the information is readily available when users are
ready to absorb it. The use of Twitter and CoI enhances students’ capabilities because
students have to research new information to create a cognizant connection noted as a
fact of the CoI framework. Students’ capacity increases because research skills and content
knowledge are heightened. Because students are using technology, their self-efficacy is devel-
oped; therefore, the students become better disseminators of information. Moreover, the
students become adept and are able to repeat the information gathering and information
dissemination processes again for new information discovery.
Next, Digital Natives and Twitter are discussed in more detail.
Twitter and Digital Natives. It is believed that Digital Natives learn differently (Kelly et al.,
2008; Prensky, 2001), and K-12 institutions and Higher Education institutions haven’t
changed teaching and learning practices to capitalize on how Digital Natives want to
learn (Kelly et al., 2008).
Research shows that students want to email, chat with instant messaging, surf the inter-
net, read and publish blogs, text with cell phones, take and send pictures with cell phones,
play electronic and online games, and listen to, watch and create podcasts (Kelly et al.,
2008).
One way education institutions can improve the educating of Digital Natives is to
embrace applications like Twitter. While Twitter does not address every way that Digital
Natives want to learn, Twitter does allow Digital Natives to learn with technology and in an
environment that they want to learn in. Similar to text messaging, blogging, and sending and
taking pictures with a cell phone, Twitter allows for students to engage in similar types of
tasks when tweeting.
Twitter allows for learning to take place anytime anywhere via a mobile device using
Twitter’s mobile app. Research shows that 86% of Digital Natives have smartphones and
are more engaged in learning when utilizing mobile devices (Hudson, 2011). To that end,
these observations further validate Twitter as an appropriate tool for teaching and learning
in education. Next, the benefits of Twitter’s having a mobile app are further discussed.
Twitter’s mobile application benefits. Studies show that over 50% of faculty believe that mobile
devices can enhance learning, while students reported that the use of mobile devices
increased communication among them (Gikas and Grant, 2013). By using mobile devices,
students can quickly access course documents and upload and post course content anywhere
(Sharples et al., 2010; Traxler, 2007, 2010).
While learning with mobile devices yields a positive impact on student success, mobile
modules are not a part of the LMS base package. Schools are facing funding issues due to

states implementing performance-based funding, low enrollment, and student retention. As a
result of lack of funding, educational institutions faced with investment decisions on whether
to acquire a mobile module may reconsider such expenditures; however, for education to
reap the benefits of mobile teaching and learning, Twitter can be used as an alternative.
Twitter has mobile apps for Apple and Android users.
Mobile code and device security. Cyber security has been a growing concern as there have been
major headlines concerning poor Information Assurance posture with the Department of
Defense (DoD), Target, Sony, and more. The used mobile device itself contains vulnerabilities
resulting from inherent weaknesses that even users may not be aware of (Vigna, 2003). This
makes it difficult to protect users, as they wish to use mobile devices for educational tweets.
Social engineering and intelligence analysis on educational tweets
With the rise of Web 2.0, Twitter has become a tool of choice for universities looking to
increase their digital footprint. However, there is not much guidance given on the protections
of these tweets or the secure integration of Twitter into other Web 2.0 applications. As a
result, this paper explores methods with which Twitter and its data can be exploited for
nefarious uses (Dawson and Omar, 2015). Explored in this paper are the methods with which
Twitter and its data can be exploited for nefarious uses.
Open Source Intelligence. The intelligence field of Open Source Intelligence (OSINT) relies on
data that is freely available on the public domain to conduct an analysis. OSINT is con-
ducted by collecting data in an overt manner. OSINT has been defined by the United States
(US) DoD and the US Director of National Intelligence as analysis produced from publicly
available information that is collected, exploited, and disseminated for the purpose of
addressing a specific intelligence requirement (Congress, 2006). Displayed in Subtitle D –
intelligence – Related Matters SEC. 931., 932., and 933 are findings by Congress for the
DoD strategy for OSINT (Congress, 2006). These findings provide a basis for the import-
ance of OSINT as it relates to intelligence.
Geolocation. Twitter allows geolocation tags in tweets through a geotagging feature in the
Twitter Application Programing Interface (API) (Twitter, 2014). Twitter provides terms of
Twitter’s Developer Policy regarding aggregation, caching, and storing of geographic infor-
mation (Twitter, 2014). However, anyone who develops an application using this API can
tweak items, providing even more granularity of its users. Even without modification of
tweets, simply adding the location will provide details such as neighborhood, city, state, or
country. This publication information can be used to start an analysis. In iOS version 6.26þ
and Android version 5.55þ, precise location can be shared if elected to do so. Also, third
party applications or websites may share precise tweet locations as well.
Personal Twitter accounts provide the ability to associate a specific location (Figure 5).
This location over time can provide trends of locations visited with time/date stamps. This
can be used to start developing a full analysis on tweeting trends from particular locations,
frequency of location visits, and content analysis through text mining.
Exchange Image File Format. Exchange Image File Format (EXIF) data is a standard that
specifies the formats for images, sounds, and ancillary tags used by digital cameras.
Cleveland et al. 11

The EXIF digital image standard specifies the following: the basic structure of digital image
data files, tags and JPEG marker segments the standard uses, and how to define and manage
format versions (Tesˇ ic´ , 2005). Research has been conducted on how to effectively extract
EXIF data for prosecuting those involved in child pornography (Alvarez, 2004).
Extracting EXIF data using the ImageMagick application is rather simple in Debian
based Linux operating systems. First, the following command is run: ‘‘sudo apt-get install
Figure 6. Twitter photo example.
Figure 5. Twitter location example.

imagemagick.’’ Next, the following command is run to extract the EXIF data: ‘‘identify -
verbose/usr/share/backgrounds/sample_exif.jpg j grep ‘‘exif:’’.’’ The EXIF data output for
Figure 6 is displayed in Figure 7.
The data that is provided includes date and time of photo taken, camera used, and other
properties that allow for a more detailed analysis over time. There are more programs than
ImageMagick that allow the viewing of EXIF data. This includes applications that can be
added to the Chrome browser, Firefox browser, and downloadable applications.
Figure 7. Exchange Image File Format data output.
Cleveland et al. 13

Stenography. Tweets can contains photos that have compressed and encrypted embedded
data. Data can be hidden in image and audio files, making it nearly impossible for institu-
tions to look at files that could contain information that is geared towards radical, extremist,
or terrorist ideologies. Researchers uncover the validity of tweets used by political reports
which can be correlated to use by nefarious organizations (Coddington et al., 2014). This
means that students, professors, or staff could be unknowingly retweeting images that can be
used for communication propaganda or illicit instructions. Figure 8 displays an example of
the extraction of a photo using Steghide, where a simple text, 123456, was embedded using
the command ‘‘steghide extract -sf pic.jpeg.’’
Text mining. The words contained in tweets provide another method of collecting intelligence
through discovery of similar words, anomaly detection, and discussion tracking (Berry and
Figure 8. Steghide stenography example.
Table 1. Open source mining tools.
Software application Description and potential use
R language Language used for statistical computing and graphics.
Maltego Program used to determine the relationships and real world links.
RapidMiner Program used for all steps of the data mining process including results
visualization, validation, and optimization.
RStudio Integrated development environment for R that allows for the use of R.
KNIME Used for enterprise reporting, Business Intelligence, data mining,
data analysis, and text mining.
Python High level, general purpose programming language.

Castellanos, 2004). Thus conversations had with others in the form of public replies can be
further analyzed for patterns, keywords, and relationship discovery. Table 1 provides a list
of software applications with their description and potential use.
Conclusion
This paper demonstrates the viability for microblogging to facilitate student knowledge
creation practices. It proposes a model to describe how specific microblogging features
can enhance the conveyance and convergence practices for the purposes of acquiring and
transferring tacit and explicit knowledge among Twitter users.
Moreover, Twitter is proposed as a legitimate tool for teaching and learning. The fact that
the use of Twitter can address several best practices when teaching today’s students/Digital
Natives further validates Twitter as a quality teaching tool. Individuals responsible for
educating Digital Natives should act now by sharing with their respective institutions how
Twitter can be used to put online teaching best practices like CoI into practice, how Twitter
can be used as a mobile app alternative, and how Twitter can be used to create a blended
learning environment desired by today’s students.
Finally, while higher education institutions continue to increase the use of Web 2.0 in
the classroom, this article provides evidence of the potential extraction of specific identi-
fiable information that Twitter posts contain. As there are a number of proprietary and
open source applications that allow for the extraction of images, the images should have
the metadata removed. The current concern of universities is the type of content shared
and not the data contained. This needs to change to ensure the overall privacy of the
individual sending out the tweets. As a result, higher education institutions are encouraged
to create policies to ensure that security and privacy of students’ educational tweets are
protected.
Declaration of conflicting interests
The authors declare that there is no conflict of interest.
Funding
This research received no specific grant from any funding agency in the public, commercial, or not-for-
profit sectors.
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Author biographies
Simon Cleveland, is an information technology industry expert with two decades of experi-
ence in managing large (up to $30 million), medium (up to $10 million) and small (up to $3
million) software systems projects for NASA, Department of Education, MD Anderson
Cancer Center, Accenture, Avanade (joint venture of Microsoft and Accenture), General
Dynamics and AOL. Dr. Cleveland was awarded a Ph.D. in Information Systems from
Nova Southeastern University. He is professionally certified Six Sigma Green Belt, project
management professional (PMP), and ITIL expert. He is also a three-time recipient of the
prestigious Dr. Harold Kerzner Scholarship awarded by the Project Management Institute
Educational Foundation. Dr. Cleveland is a part time IS professor at Northeastern
University, Brandeis University, and Nova Southeastern University.
Barcus C Jackson, ITIL is the Director of Simulation and Instructional Technology at
Goldfarb School of Nursing at Barnes-Jewish College. In the past roles, Jackson was at
Alton Community Unit School District #11 where he worked as the Director of Data and

Technology Services and University City School District where he served as Chief
Information Officer. He also has ten years teaching experience at the University level and
has served as an Assistant professor of Information Technology and Business and as an
adjunct professor for multiple colleges and universities. Jackson received his doctor of
education, leadership from Missouri Baptist University-St. Louis, his Doctor of Computer
Science from Colorado Technical University-Colorado Springs, Masters of Business
Administration from Fontbonne University-St. Louis and a Bachelor of Science in compu-
ters from Lane College-Jackson, Tennessee. Jackson is a member of the Association of
Information Technology Professionals, Educause, and the Project Management Institute.
Additionally, he holds the ITIL (formerly known as Information Technology Infrastructure
Library), CompTIA A+, Network+, and Security+ certifications.
Barcus C Jackson, is an Assistant Professor of Information Systems (Cyber Security) at the
College of Business Administration at University of Missouri- St. Louis with affiliation to
the Cyber-security and Information Technology Innovation Lab (CITIL). Visiting Professor
at the Polytechnic University of Puerto Rico, Visiting Professor at the University of Nairobi,
and a Senior Research Fellow at the American Leadership & Policy Foundation (ALPF).
Additionally, he was a Visiting Assistant Professor (Honorary) at the University of
Tennessee in the College of Engineering within the Department of Industrial and Systems
Engineering. Dawson has received a Fulbright Scholar grant to Russia in 2014. Dawson
served as a Visiting Scholar with the University of The Gambia from 2014–2015.
Cleveland et al. 19

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