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Content analysis: an introduction to its methodology
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History Conceptual Foundations Uses and Kinds of Inference The Logic of Content Analysis Designs Unitizing Sampling Recording Data Languages Constructs for Inference Analytical Techniques The Use of Computers Reliability Validity A Practical GuideAbstract:
History Conceptual Foundations Uses and Kinds of Inference The Logic of Content Analysis Designs Unitizing Sampling Recording Data Languages Constructs for Inference Analytical Techniques The Use of Computers Reliability Validity A Practical Guideread more
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Journal ArticleDOI
Rethinking validity and reliability in content analysis
TL;DR: It is argued that validity and reliability should be conceptualized differently across the various forms of content and the various uses of theory, and that content analyses need not be limited to theory‐based coding schemes and standards set by experts.
Journal ArticleDOI
Assessing teaching presence in a computer conferencing context
TL;DR: A tool developed for the purpose of assessing teaching presence in online courses that make use of computer conferencing is presented, and preliminary results from the use of this tool are revealed.
Journal ArticleDOI
Content analysis: review of methods and their applications in nutrition education
TL;DR: Options available to content analysts--from manual to fully computerized are reviewed, recommended because of their usefulness in the information-based messaging discipline of nutrition education.
Proceedings ArticleDOI
Political Polarization on Twitter
Michael Conover,Jacob Ratkiewicz,Matthew Francisco,Bruno Gonçalves,Filippo Menczer,Alessandro Flammini +5 more
TL;DR: It is demonstrated that the network of political retweets exhibits a highly segregated partisan structure, with extremely limited connectivity between left- and right-leaning users, and surprisingly this is not the case for the user-to-user mention network, which is dominated by a single politically heterogeneous cluster of users.
Journal IssueDOI
Sentiment in short strength detection informal text
TL;DR: SentiStrength as discussed by the authors is able to predict positive emotion with 60.6p accuracy and negative emotion with 72.8p accuracy, both based upon strength scales of 1-5.