Twitter Archeology" of learning analytics and knowledge conferences (original) (raw)
Related papers
Computer Communications, 2016
Twitter is often referred to as a backchannel for conferences. While the main conference takes place in a physical setting, on-site and off-site attendees socialize, introduce new ideas or broadcast information by microblogging on Twitter. In this paper we analyze scholars' Twitter usage in 16 Computer Science conferences over a timespan of five years. Our primary finding is that over the years there are differences with respect to the uses of Twitter, with an increase of informational activity (retweets and URLs), and a decrease of conversational usage (replies and mentions), which also impacts the network structure-meaning the amount of connected components-of the informational and conversational networks. We also applied topic modeling over the tweets' content and found that when clustering conferences according to their topics the resulting dendrogram clearly reveals the similarities and differences of the actual research interests of those events. Furthermore, we also analyzed the sentiment of tweets and found persistent differences among conferences. It also shows that some communities consistently express messages with higher levels of emotions while others do it in a more neutral manner. Finally, we investigated some features that can help predict future user participation in the online Twitter conference activity. By casting the problem as a classification task, we created a model that identifies factors that contribute to the continuing user participation. Our results have implications for research communities to implement strategies for continuous and active participation among members. Moreover, our work reveals the potential for the use of information shared on Twitter in order to facilitate communication and cooperation among research communities, by providing visibility to new resources or researchers from relevant but often little known research communities.
Twitter in academic conferences: usage, networking and participation over time
Twitter is often referred to as a backchannel for conferences. While the main conference takes place in a physical setting, attendees and virtual attendees socialize, introduce new ideas or broadcast information by microblogging on Twitter. In this paper we analyze the scholars' Twitter use in 16 Computer Science conferences over a timespan of five years. Our primary finding is that over the years there are increasing differences with respect to conversation use and information use in Twitter. We studied the interaction network between users to understand whether assumptions about the structure of the conversations hold over time and between different types of interactions, such as retweets, replies, and mentions. While 'people come and people go', we want to understand what keeps people stay with the conference on Twitter. By casting the problem to a classification task, we find different factors that contribute to the continuing participation of users to the online Twitter conference activity. These results have implications for research communities to implement strategies for continuous and active participation among members.
The scholarly community faces a lack of large-scale research examining how students and professors use social media in authentic contexts and how such use changes over time. This study uses data mining methods to better understand academic Twitter use during, around, and between the 2014 and 2015 American Educational Research Association annual conferences both as a conference backchannel and as a general means of participating online. Descriptive and inferential analysis is used to explore Twitter use for 1421 academics and the more than 360 000 tweets they posted. Results demonstrate the complicated participation patterns of how Twitter is used " on the ground. " In particular, we show that tweets during conferences differed significantly from tweets outside conferences. Further, students and professors used the conference backchannel somewhat equally, but students used some hashtags more frequently, while professors used other hashtags more frequently. Academics comprised the minority of participants in these backchannels, but participated at a much higher rate than their non-academic counterparts. While the number of participants in the backchannel increased between 2014 and 2015, only a small number of authors were present during both years, and the number of tweets declined from year to year. Various hashtags were used throughout the time period during which this study occurred, and some were ongoing (ie, those which tended to be stable across weeks) while others were event-based (ie, those which spiked in a particular week). Professors used event-based hashtags more often than students and students used ongoing hashtags more often than professors. Ongoing hashtags tended to exhibit positive sentiment, while event-based hashtags tended to exhibit more ambiguous or conflicting sentiments. These findings suggest that professors and students exhibit similarities and differences in how they use Twitter and backchannels and indicate the need for further research to better understand the ways that social technologies and online networks are integrated in scholars' lives.
Computer Supported Cooperative Work (CSCW)
As social media become a staple for knowledge discovery and sharing, questions arise about how self-organizing communities manage learning outside the domain of organized, authority-led institutions. Yet examination of such communities is challenged by the quantity of posts and variety of media now used for learning. This paper addresses the challenges of identifying (1) what information, communication, and discursive practices support successful online communities, (2) whether such practices are similar on Twitter and Reddit, and (3) whether machine learning classifiers can be successfully used to analyze larger datasets of learning exchanges. This paper builds on earlier work that used manual coding of learning and exchange in Reddit 'Ask' communities to derive a coding schema we refer to as 'learning in the wild'. This schema of eight categories: explanation with disagreement, agreement, or neutral presentation; socializing with negative, or positive intent; information seeking; providing resources; and comments about forum rules and norms. To compare across media, results from coding Reddit's AskHistorians are compared to results from coding a sample of #Twitterstorians tweets (n = 594). High agreement between coders affirmed the applicability of the coding schema to this different medium. LIWC lexicon-based text analysis was used to build machine learning classifiers and apply these to code a larger dataset of tweets (n = 69,101). This research shows that the 'learning in the wild' coding schema holds across at least two different platforms, and is partially scalable to study larger online learning communities.
MOOC Friends and Followers: An Analysis of Twitter Hashtag Networks
In this paper we present results of the initial phase of a project which sought to analyze the community who use the hashtag #MOOC in Twitter. We conceptualize this community as a form of networked public. In doing so we ask what the nature of this public is and whether it may be best conceived of as a social or informational network. In addition we seek to uncover who the stakeholders are who most influentially participate. We do this by using Social Network Analysis (SNA) to uncover the key hubs and influencers in the network. We use two approaches to deriving a network typology-one based on follows and on based on replies and compare and contrast the results.
Hashtags and retweets: using Twitter to aid Community, Communication and Casual (informal) learning
Since the evolution of Web 2.0, or the Social Web, the way in which users interact with/on the Internet has seen a massive paradigm shift. Web 2.0 tools and technologies have completely changed the dynamics of the Internet, enabling users to create content; be it text, photographs or video; and furthermore share and collaborate across massive geographic boundaries. As part of this revolution, arguably the most significant tools have been those employing social media. This research project set out to investigate student's attitudes, perceptions and activity toward the use of Twitter in supporting learning and teaching. In so doing, this paper touches on a number of current debates in higher education, such as the role (and perceived rise) of informal learning; and debates around Digital Natives/ Immigrants vs. Digital Residents/Visitors. In presenting early research findings, the author considers the 3Cs of Twitter (T3c): Community, Communication and Casual (informal) learning. Data suggests that students cannot be classed as Digital Natives purely on age and suggests a rethinking of categorisations is necessary. Furthermore, the data suggests students are developing their own personal learning environments (PLEs) based on user choice. Those students who voluntarily engaged with Twitter during this study positively evaluated the tool for use within learning and teaching.
What Does Twitter Say About Self-Regulated Learning? Mapping Tweets From 2011 to 2021
Frontiers in Psychology, 2022
Social network services such as Twitter are important venues that can be used as rich data sources to mine public opinions about various topics. In this study, we used Twitter to collect data on one of the most growing theories in education, namely Self-Regulated Learning (SRL) and carry out further analysis to investigate What Twitter says about SRL? This work uses three main analysis methods, descriptive, topic modeling, and geocoding analysis. The searched and collected dataset consists of a large volume of relevant SRL tweets equal to 54,070 tweets between 2011 and 2021. The descriptive analysis uncovers a growing discussion on SRL on Twitter from 2011 till 2018 and then markedly decreased till the collection day. For topic modeling, the text mining technique of Latent Dirichlet allocation (LDA) was applied and revealed insights on computationally processed topics. Finally, the geocoding analysis uncovers a diverse community from all over the world, yet a higher density representation of users from the Global North was identified. Further implications are discussed in the paper.
Educational influencers on Twitter. Analysis of hashtags and relationship structure
Comunicar, 2021
n this article we research Spanish educational influencers with major presence on Twitter: what are the most common topics or hashtags used by them, whether there are groups based on the topics of their interventions or what type of social network they configure. To meet these goals, we selected 54 educational influencers with a high number of followers. We analyzed and classified the "hashtags" included in a total of 106,130 tweets. The analysis of hashtags has shown us that the most labelled topics correspond to educational content in different areas of the curriculum, collaboration, exchange and dissemination of digital materials, documents or resources, as well as activities related to training or discussion about innovative teaching methodologies. Using the Gephi software, we carried out a Social Network Analysis, determining the degree of centrality and betweenness centrality of the 54 influencers, which allowed us to identify influencers with greater recognition by the rest. Through a modularity analysis, we were able to identify five groups of influencers that do not work as closed groups but maintain frequent interactions with other influencers in other groups. This research highlights the need to better understand the contents and procedures that may promote informal learning by teachers.
@twitter analysis of #edmediaXX – is the informationstream usable for the #mass
2013
In this paper we report the use of an application that enables an automatic analyses of social media content. In this early stage of development our work focuses on data from Twitter 1 as currently to be the most popular and fastest growing microblogging platform. After an introduction about a general concept the conference tweets of a big e-learning conference are examined twice. It is aimed to show whether there is a possibility to get significant information from a pool of postings or not. The publication concludes that a keyword extraction can be taken as basis for further investigations and treatment of data.