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- Volume 3, Issue 1, 2021
Computational Communication Research - Volume 3, Issue 1, 2021
Volume 3, Issue 1, 2021
Language:
English
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oa Four best practices for measuring news sentiment using ‘off-the-shelf’ dictionaries: a large-scale p-hacking experiment
Abstract We examined the validity of 37 sentiment scores based on dictionary-based methods using a large news corpus and demonstrated the risk of generating a spectrum of results with different levels of statistical significance by presenting an analysis of relationships between news sentiment and U.S. presidential approval. We summarize our findings into four best practices: 1) use a suitable sentiment dictionary; 2) do not assu Read More
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oa A Weakly Supervised and Deep Learning Method for an Additive Topic Analysis of Large Corpora
Authors: Yair Fogel-Dror, Shaul R. Shenhav & Tamir SheaferAbstract The collaborative effort of theory-driven content analysis can benefit significantly from the use of topic analysis methods, which allow researchers to add more categories while developing or testing a theory. This additive approach enables the reuse of previous efforts of analysis or even the merging of separate research projects, thereby making these methods more accessible and increasing the discipline’s ability t Read More
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oa Statistical Power in Content Analysis Designs: How Effect Size, Sample Size and Coding Accuracy Jointly Affect Hypothesis Testing – A Monte Carlo Simulation Approach.
By Stefan GeißAbstract This study uses Monte Carlo simulation techniques to estimate the minimum required levels of intercoder reliability in content analysis data for testing correlational hypotheses, depending on sample size, effect size and coder behavior under uncertainty. The ensuing procedure is analogous to power calculations for experimental designs. In most widespread sample size/effect size settings, the rule-of-thumb that ch Read More
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oa Down to a r/science: Integrating Computational Approaches to the Study of Credibility on Reddit
Authors: Austin Hubner, Jessica McKnight, Matthew Sweitzer & Robert BondAbstract Digital trace data enable researchers to study communication processes at a scale previously impossible. We combine social network analysis and automated content analysis to examine source and message factors’ impact on ratings of user-shared content. We found that the expertise of the author, the network position that the author occupies, and characteristics of the content the author creates have a signi Read More
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oa A tool for tracking the propagation of words on Reddit
Authors: Tom Willaert, Paul Van Eecke, Jeroen Van Soest & Katrien BeulsAbstract The data-driven study of cultural information diffusion in online (social) media is currently an active area of research. The availability of data from the web thereby generates new opportunities to examine how words propagate through online media and communities, as well as how these diffusion patterns are intertwined with the materiality and culture of social media platforms. In support of such efforts, this paper i Read More
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oa Computational observation
Authors: Mario Haim & Angela Nienierza
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