Spam and Educators' Twitter Use
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“we suggest practical, holistic metrics that can be employed to help identify spam.” Although educators use a variety of social media to advance their own learning, Twitter has been particularly popular among both educators organizing their own professional learning and the researchers studying them (e.g., Carpenter and Krutka 2014; Rosenberg, Greenhalgh et al. 2016; Tucker 2019; Veletsianos 2017). One of the features of Twitter that makes this platform especially helpful for creating professional learning commu- nities is the hashtag (i.e., a word or phrase, preceded by a hash or pound sign, that organizes content by topics). Education- focused Twitter hashtags provide educators with spaces to connect, share ideas and discuss education topics (Carpenter et al. 2018; Rosenberg et al. 2016). These spaces have been evaluated in terms of communities of practice (e.g., Britt and Paulus 2016; Gao and Li 2017), professional learning communities (e.g., Goodyear et al. 2019) and affinity spaces (e.g., Staudt Willet 2019; Rosenberg et al. 2016). Although there are important distinctions between these frameworks, the literature generally concludes that professional learning spaces created through Twitter hashtags can be of real value to educators. It is therefore unsurprising that some teacher educators now explicitly introduce Twitter and Twitter hashtags to preservice teachers (e.g., Carpenter 2015; Carpenter and Morrison 2018; Hsieh 2017; Luo et al. 2017). Yet, the same Twitter features that allow for the easy creation of spaces for professional learning also allow spam—“unsolic- ited, repeated actions that negatively impact other people” * Jeffrey P. Carpenter [email protected] K. Bret Staudt Willet [email protected] Matthew J. Koehler [email protected] Spencer P.
“Twitter and other social media have assumed important places in many educators’ professional lives by hosting spaces where new kinds of collegial interactions can occur.” Although educators use a variety of social media to advance their own learning, Twitter has been particularly popular among both educators organizing their own professional learning and the researchers studying them (e.g., Carpenter and Krutka 2014; Rosenberg, Greenhalgh et al. 2016; Tucker 2019; Veletsianos 2017). One of the features of Twitter that makes this platform especially helpful for creating professional learning commu- nities is the hashtag (i.e., a word or phrase, preceded by a hash or pound sign, that organizes content by topics). Education- focused Twitter hashtags provide educators with spaces to connect, share ideas and discuss education topics (Carpenter et al. 2018; Rosenberg et al. 2016). These spaces have been evaluated in terms of communities of practice (e.g., Britt and Paulus 2016; Gao and Li 2017), professional learning communities (e.g., Goodyear et al. 2019) and affinity spaces (e.g., Staudt Willet 2019; Rosenberg et al. 2016). Although there are important distinctions between these frameworks, the literature generally concludes that professional learning spaces created through Twitter hashtags can be of real value to educators. It is therefore unsurprising that some teacher educators now explicitly introduce Twitter and Twitter hashtags to preservice teachers (e.g., Carpenter 2015; Carpenter and Morrison 2018; Hsieh 2017; Luo et al. 2017). Yet, the same Twitter features that allow for the easy creation of spaces for professional learning also allow spam—“unsolic- ited, repeated actions that negatively impact other people” * Jeffrey P. Carpenter [email protected] K. Bret Staudt Willet [email protected] Matthew J. Koehler [email protected] Spencer P.
ORIGINAL PAPER Spam and Educators’ Twitter Use: Methodological Challenges and Considerations Jeffrey P. Carpenter 1 & K. Bret Staudt Willet 2 & Matthew J. Koehler 2 & Spencer P. Greenhalgh 3 # Association for Educational Communications & Technology 2019 Abstract Twitter and other social media have assumed important places in many educators’ professional lives by hosting spaces where new kinds of collegial interactions can occur. However, such spaces can also attract unwelcome Twitter traffic that complicates researchers’ attempts to explore and understand educators’ professional social media experiences. In this article, we define various kinds of spam that we have identified in our research on educators’ uses of Twitter. After providing an overview of the concept of spam, we evaluate the advantages and disadvantages of different approaches to addressing the presence of spam in educator-focused Twitter spaces. Then we suggest practical, holistic metrics that can be employed to help identify spam. Through secondary analyses of our past research, we describe the use of such metrics to identify and deal with spam in three specific cases. Finally, we discuss implications of spam and these suggested methods for teacher educators, instructional designers and educa- tional technology researchers. Keywords Hashtags . Professional community . Professional learning . Research methods . Spam . Social media . Twitter Researchers are increasingly taking interest in how educators use social media platforms for professional learning purposes. Technology has long been recognized as offering new learn- ing opportunities to educators (e.g., Dede 2006; Macià and García 2016; Tour 2017), and the specific role of social media platforms has recently received increased attention (e.g., Prestridge et al. 2019). “a word or phrase, preceded by a hash or pound sign, that organizes content by topics” Greenhalgh [email protected] 1 Elon University, 336 278 5969, Campus Box 2105, Elon, NC 27278, USA 2 Michigan State University, 620 Farm Ln, East Lansing, MI 48824, USA 3 University of Kentucky, 320 Lucille Little Library Bldg., Lexington, KY 40506, USA TechTrends https://doi.org/10.1007/s11528-019-00466-3
“we suggest practical, holistic metrics that can be employed to help identify spam.” Although educators use a variety of social media to advance their own learning, Twitter has been particularly popular among both educators organizing their own professional learning and the researchers studying them (e.g., Carpenter and Krutka 2014; Rosenberg, Greenhalgh et al. 2016; Tucker 2019; Veletsianos 2017). One of the features of Twitter that makes this platform especially helpful for creating professional learning commu- nities is the hashtag (i.e., a word or phrase, preceded by a hash or pound sign, that organizes content by topics). Education- focused Twitter hashtags provide educators with spaces to connect, share ideas and discuss education topics (Carpenter et al. 2018; Rosenberg et al. 2016). These spaces have been evaluated in terms of communities of practice (e.g., Britt and Paulus 2016; Gao and Li 2017), professional learning communities (e.g., Goodyear et al. 2019) and affinity spaces (e.g., Staudt Willet 2019; Rosenberg et al. 2016). Although there are important distinctions between these frameworks, the literature generally concludes that professional learning spaces created through Twitter hashtags can be of real value to educators. It is therefore unsurprising that some teacher educators now explicitly introduce Twitter and Twitter hashtags to preservice teachers (e.g., Carpenter 2015; Carpenter and Morrison 2018; Hsieh 2017; Luo et al. 2017). Yet, the same Twitter features that allow for the easy creation of spaces for professional learning also allow spam—“unsolic- ited, repeated actions that negatively impact other people” * Jeffrey P. Carpenter [email protected] K. Bret Staudt Willet [email protected] Matthew J. Koehler [email protected] Spencer P.
“such spaces can also attract unwelcome Twitter traffic that complicates researchers’ attempts to explore and understand educators’ professional social media experiences.” Although educators use a variety of social media to advance their own learning, Twitter has been particularly popular among both educators organizing their own professional learning and the researchers studying them (e.g., Carpenter and Krutka 2014; Rosenberg, Greenhalgh et al. 2016; Tucker 2019; Veletsianos 2017). One of the features of Twitter that makes this platform especially helpful for creating professional learning commu- nities is the hashtag (i.e., a word or phrase, preceded by a hash or pound sign, that organizes content by topics). Education- focused Twitter hashtags provide educators with spaces to connect, share ideas and discuss education topics (Carpenter et al. 2018; Rosenberg et al. 2016). These spaces have been evaluated in terms of communities of practice (e.g., Britt and Paulus 2016; Gao and Li 2017), professional learning communities (e.g., Goodyear et al. 2019) and affinity spaces (e.g., Staudt Willet 2019; Rosenberg et al. 2016). Although there are important distinctions between these frameworks, the literature generally concludes that professional learning spaces created through Twitter hashtags can be of real value to educators. It is therefore unsurprising that some teacher educators now explicitly introduce Twitter and Twitter hashtags to preservice teachers (e.g., Carpenter 2015; Carpenter and Morrison 2018; Hsieh 2017; Luo et al. 2017). Yet, the same Twitter features that allow for the easy creation of spaces for professional learning also allow spam—“unsolic- ited, repeated actions that negatively impact other people” * Jeffrey P. Carpenter [email protected] K. Bret Staudt Willet [email protected] Matthew J. Koehler [email protected] Spencer P.
Greenhalgh [email protected] 1 Elon University, 336 278 5969, Campus Box 2105, Elon, NC 27278, USA 2 Michigan State University, 620 Farm Ln, East Lansing, MI 48824, USA 3 University of Kentucky, 320 Lucille Little Library Bldg., Lexington, KY 40506, USA TechTrends https://doi.org/10.1007/s11528-019-00466-3 “this low threshold allows for irrelevant, off-topic, commercial, or malicious mes- sages to easily enter the space.” In any of these cases, however, identify- ing spam and establishing a plan for what to do with it is a key step, albeit an often-overlooked one. Background In the following sections, we describe some basic characteris- tics of spam as well as how personal and contextual factors may influence an individual’s perceptions of those characteristics. In doing so, we draw on selected sources from the broader litera- ture on spam while concentrating on how spam and spam-like activity have been acknowledged in Twitter research by educa- tional technology scholars. We conclude with a brief summary of how researchers have responded to spam. Characteristics of Spam Spam has been described as “undesirable text, whether repet- itive, excessive, or interfering” (Brunton 2013, p. xxii). This broad definition is helpful for acknowledging the wide range of forms that spam can take, but attention to some of the specific ways text can be undesirable (especially within an education-focused community) is necessary for fully under- standing this phenomenon. These are described below and exemplified in the faux tweets in Fig. 1. & Unintentionality: The same hashtag may mean different things to different people, leading to two different groups accidentally occupying the same space. Similarly, mis- takes or typos may lead to someone posting to an educa- tional hashtag entirely by accident. For example, in Fig. 1a, the “#maet” hashtag is used by participants in a Master of Arts in Educational Technology program, but the functionally-identical “#mæt” hashtag refers to the Danish word for “full” (see Greenhalgh et al. 2016). & Irrelevance: Hashtags are open spaces, which lowers bar- riers to participation for both educators and those speaking on other, unrelated topics. & Commercial nature: Among the unrelated messages that may enter an educational hashtag are those promoting commercial products (whether educational or not).
Greenhalgh [email protected] 1 Elon University, 336 278 5969, Campus Box 2105, Elon, NC 27278, USA 2 Michigan State University, 620 Farm Ln, East Lansing, MI 48824, USA 3 University of Kentucky, 320 Lucille Little Library Bldg., Lexington, KY 40506, USA TechTrends https://doi.org/10.1007/s11528-019-00466-3 “Because popular Twitter hashtags attract attention from a considerable audience, such hashtags can prove an enticing target for spammers who attempt to redirect users’ attention.” In any of these cases, however, identify- ing spam and establishing a plan for what to do with it is a key step, albeit an often-overlooked one. Background In the following sections, we describe some basic characteris- tics of spam as well as how personal and contextual factors may influence an individual’s perceptions of those characteristics. In doing so, we draw on selected sources from the broader litera- ture on spam while concentrating on how spam and spam-like activity have been acknowledged in Twitter research by educa- tional technology scholars. We conclude with a brief summary of how researchers have responded to spam. Characteristics of Spam Spam has been described as “undesirable text, whether repet- itive, excessive, or interfering” (Brunton 2013, p. xxii). This broad definition is helpful for acknowledging the wide range of forms that spam can take, but attention to some of the specific ways text can be undesirable (especially within an education-focused community) is necessary for fully under- standing this phenomenon. These are described below and exemplified in the faux tweets in Fig. 1. & Unintentionality: The same hashtag may mean different things to different people, leading to two different groups accidentally occupying the same space. Similarly, mis- takes or typos may lead to someone posting to an educa- tional hashtag entirely by accident. For example, in Fig. 1a, the “#maet” hashtag is used by participants in a Master of Arts in Educational Technology program, but the functionally-identical “#mæt” hashtag refers to the Danish word for “full” (see Greenhalgh et al. 2016). & Irrelevance: Hashtags are open spaces, which lowers bar- riers to participation for both educators and those speaking on other, unrelated topics. & Commercial nature: Among the unrelated messages that may enter an educational hashtag are those promoting commercial products (whether educational or not).
It is not difficult to imagine how encountering excessive spam may obscure the potential benefits of professional networking and collaboration via Twitter. It may therefore behoove teacher TechTrends “educators should con- sider the ways that commercial motivations could be more subtly reflected in some Twitter posts, such as through con- nections between tweeters and educational technology prod- ucts that can only be discovered by careful attention to an account’s Twitter profile or visiting the website to which a profile links” Conclusion We suggest that addressing spam should be a critical step for research studying online educator spaces and communities. Researchers should acknowledge—and potentially explore— the kinds of impacts spam may have on their data and describe steps they take to mitigate those impacts, even when spam is not the central concern of their research. This would advance un- derstanding of the roles of social media in education by allowing for better comparisons of results across studies. Given the large amounts of educator professional activities that occur online, spam’s impacts are important for educational re- searchers, teacher educators, and instructional designers to con- sider. Spam likely affects educators’ social media use and it additionally creates challenges for researchers as they collect and analyze data. In many cases, spam can confound under- standing of the phenomenon of interest. In this paper, we have described practical metrics and a holistic decision-making strat- egy that should prove useful and accessible for many educa- tional technology researchers. We assert the value of human decision-making regarding spam and suggest that scholars should keep in mind the diversity of spam and the various ways that educators may analyze and react to it. Compliance with Ethical Standards Disclosure of Potential Conflicts of Interest The authors declare that they have no conflict of interest. Research Involving Human Participants and/or Animals This article contains no studies with animals performed by the authors. All actions performed in studies involving human participants were conducted in accordance with the ethical standards of the relevant institutional review board, and also with the 1964 Helsinki declaration and its later amend- ments or comparable ethical standards. Informed Consent This paper conducts secondary analysis of data col- lected during two earlier studies (Carpenter et al. 2018; Staudt Willet 2019).
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