update bibliography

Bibliography.bib

@@ -21,9 +21,8 @@
   date = {2021-05-20},
   eprint = {2101.08750},
   eprinttype = {arxiv},
-  primaryclass = {cs},
+  eprintclass = {cs},
   abstract = {The QAnon conspiracy theory claims that a cabal of (literally) blood-thirsty politicians and media personalities are engaged in a war to destroy society. By interpreting cryptic "drops" of information from an anonymous insider calling themself Q, adherents of the conspiracy theory believe that Donald Trump is leading them in an active fight against this cabal. QAnon has been covered extensively by the media, as its adherents have been involved in multiple violent acts, including the January 6th, 2021 seditious storming of the US Capitol building. Nevertheless, we still have relatively little understanding of how the theory evolved and spread on the Web, and the role played in that by multiple platforms. To address this gap, we study QAnon from the perspective of "Q" themself. We build a dataset of 4,949 canonical Q drops collected from six "aggregation sites," which curate and archive them from their original posting to anonymous and ephemeral image boards. We expose that these sites have a relatively low (overall) agreement, and thus at least some Q drops should probably be considered apocryphal. We then analyze the Q drops' contents to identify topics of discussion and find statistically significant indications that drops were not authored by a single individual. Finally, we look at how posts on Reddit are used to disseminate Q drops to wider audiences. We find that dissemination was (initially) limited to a few sub-communities and that, while heavy-handed moderation decisions have reduced the overall issue, the "gospel" of Q persists on the Web.},
-  archiveprefix = {arXiv},
   keywords = {Computer Science - Computers and Society,Computer Science - Social and Information Networks},
   file = {/home/nathante/Zotero/storage/V96424CW/Aliapoulios et al_2021_The Gospel According to Q.pdf;/home/nathante/Zotero/storage/USF2Z7ZX/2101.html}
 }
@@ -31,8 +30,7 @@
 @article{araujo_automated_2020,
   title = {Automated {{Visual Content Analysis}} ({{AVCA}}) in {{Communication Research}}: {{A Protocol}} for {{Large Scale Image Classification}} with {{Pre-Trained Computer Vision Models}}},
   shorttitle = {Automated {{Visual Content Analysis}} ({{AVCA}}) in {{Communication Research}}},
-  author = {Araujo, Theo and Lock, Irina and van de Velde, Bob},
+  author = {Araujo, Theo and Lock, Irina and family=Velde, given=Bob, prefix=van de, useprefix=true},
-  options = {useprefix=true},
   date = {2020-10-01},
   journaltitle = {Communication Methods and Measures},
   volume = {14},
@@ -41,7 +39,6 @@
   publisher = {{Routledge}},
   issn = {1931-2458},
   abstract = {The increasing volume of images published online in a wide variety of contexts requires communication researchers to address this reality by analyzing visual content at a large scale. Ongoing advances in computer vision to automatically detect objects, concepts, and features in images provide a promising opportunity for communication research. We propose a research protocol for Automated Visual Content Analysis (AVCA) to enable large-scale content analysis of images. It offers inductive and deductive ways to use commercial pre-trained models for theory building in communication science. Using the example of corporations’ website images on sustainability, we show in a step-by-step fashion how to classify a large sample (N = 21,876) of images with unsupervised and supervised machine learning, as well as custom models. The possibilities and pitfalls of these approaches are discussed, ethical issues are addressed, and application examples for future communication research are detailed.},
-  annotation = {\_eprint: https://doi.org/10.1080/19312458.2020.1810648},
   file = {/home/nathante/Zotero/storage/YUAKMGKV/Araujo et al_2020_Automated Visual Content Analysis (AVCA) in Communication Research.pdf}
 }
 
@@ -62,8 +59,7 @@
 @article{baden_three_2022,
   title = {Three {{Gaps}} in {{Computational Text Analysis Methods}} for {{Social Sciences}}: {{A Research Agenda}}},
   shorttitle = {Three {{Gaps}} in {{Computational Text Analysis Methods}} for {{Social Sciences}}},
-  author = {Baden, Christian and Pipal, Christian and Schoonvelde, Martijn and van der Velden, Mariken A. C. G},
+  author = {Baden, Christian and Pipal, Christian and Schoonvelde, Martijn and family=Velden, given=Mariken A. C. G, prefix=van der, useprefix=true},
-  options = {useprefix=true},
   date = {2022-01-02},
   journaltitle = {Communication Methods and Measures},
   shortjournal = {Communication Methods and Measures},
@@ -148,8 +144,7 @@
 @article{boukes_whats_2020,
   title = {What’s the {{Tone}}? {{Easy Doesn}}’t {{Do It}}: {{Analyzing Performance}} and {{Agreement Between Off-the-Shelf Sentiment Analysis Tools}}},
   shorttitle = {What’s the {{Tone}}?},
-  author = {Boukes, Mark and van de Velde, Bob and Araujo, Theo and Vliegenthart, Rens},
+  author = {Boukes, Mark and family=Velde, given=Bob, prefix=van de, useprefix=true and Araujo, Theo and Vliegenthart, Rens},
-  options = {useprefix=true},
   date = {2020-04-02},
   journaltitle = {Communication Methods and Measures},
   volume = {14},
@@ -158,7 +153,6 @@
   publisher = {{Routledge}},
   issn = {1931-2458},
   abstract = {This article scrutinizes the method of automated content analysis to measure the tone of news coverage. We compare a range of off-the-shelf sentiment analysis tools to manually coded economic news as well as examine the agreement between these dictionary approaches themselves. We assess the performance of five off-the-shelf sentiment analysis tools and two tailor-made dictionary-based approaches. The analyses result in five conclusions. First, there is little overlap between the off-the-shelf tools; causing wide divergence in terms of tone measurement. Second, there is no stronger overlap with manual coding for short texts (i.e., headlines) than for long texts (i.e., full articles). Third, an approach that combines individual dictionaries achieves a comparably good performance. Fourth, precision may increase to acceptable levels at higher levels of granularity. Fifth, performance of dictionary approaches depends more on the number of relevant keywords in the dictionary than on the number of valenced words as such; a small tailor-made lexicon was not inferior to large established dictionaries. Altogether, we conclude that off-the-shelf sentiment analysis tools are mostly unreliable and unsuitable for research purposes – at least in the context of Dutch economic news – and manual validation for the specific language, domain, and genre of the research project at hand is always warranted.},
-  annotation = {\_eprint: https://doi.org/10.1080/19312458.2019.1671966},
   file = {/home/nathante/Zotero/storage/HXRTCXAZ/Boukes et al_2020_What’s the Tone.pdf}
 }
 
@@ -200,8 +194,7 @@
   pages = {141--155},
   publisher = {{Routledge}},
   issn = {1931-2458},
-  abstract = {In this work, we evaluate different instruction strategies to improve the quality of crowdcoding for the concept of civility. We test the effectiveness of training, codebooks, and their combination through 2 × 2 experiments conducted on two different populations – students and Amazon Mechanical Turk workers. In addition, we perform simulations to evaluate the trade-off between cost and performance associated with different instructional strategies and the number of human coders. We find that training improves crowdcoding quality, while codebooks do not. We further show that relying on several human coders and applying majority rule to their assessments significantly improves performance.},
+  abstract = {In this work, we evaluate different instruction strategies to improve the quality of crowdcoding for the concept of civility. We test the effectiveness of training, codebooks, and their combination through 2 × 2 experiments conducted on two different populations – students and Amazon Mechanical Turk workers. In addition, we perform simulations to evaluate the trade-off between cost and performance associated with different instructional strategies and the number of human coders. We find that training improves crowdcoding quality, while codebooks do not. We further show that relying on several human coders and applying majority rule to their assessments significantly improves performance.}
-  annotation = {\_eprint: https://doi.org/10.1080/19312458.2021.1895977}
 }
 
 @book{buonaccorsi_measurement_2010,
@@ -236,8 +229,7 @@
 @article{burscher_teaching_2014,
   title = {Teaching the {{Computer}} to {{Code Frames}} in {{News}}: {{Comparing Two Supervised Machine Learning Approaches}} to {{Frame Analysis}}},
   shorttitle = {Teaching the {{Computer}} to {{Code Frames}} in {{News}}},
-  author = {Burscher, Björn and Odijk, Daan and Vliegenthart, Rens and de Rijke, Maarten and de Vreese, Claes H.},
+  author = {Burscher, Björn and Odijk, Daan and Vliegenthart, Rens and family=Rijke, given=Maarten, prefix=de, useprefix=true and family=Vreese, given=Claes H., prefix=de, useprefix=true},
-  options = {useprefix=true},
   date = {2014-07-03},
   journaltitle = {Communication Methods and Measures},
   shortjournal = {Communication Methods and Measures},
@@ -379,7 +371,6 @@
   publisher = {{Routledge}},
   issn = {1931-2458},
   abstract = {Dictionary-based approaches to computational text analysis have been shown to perform relatively poorly, particularly when the dictionaries rely on simple bags of words, are not specified for the domain under study, and add word scores without weighting. While machine learning approaches usually perform better, they offer little insight into (a) which of the assumptions underlying dictionary approaches (bag-of-words, domain transferability, or additivity) impedes performance most, and (b) which language features drive the algorithmic classification most strongly. To fill both gaps, we offer a systematic assumption-based error analysis, using the integrative complexity of social media comments as our case in point. We show that attacking the additivity assumption offers the strongest potential for improving dictionary performance. We also propose to combine off-the-shelf dictionaries with supervised “glass box” machine learning algorithms (as opposed to the usual “black box” machine learning approaches) to classify texts and learn about the most important features for classification. This dictionary-plus-supervised-learning approach performs similarly well as classic full-text machine learning or deep learning approaches, but yields interpretable results in addition, which can inform theory development on top of enabling a valid classification.},
-  annotation = {\_eprint: https://doi.org/10.1080/19312458.2021.1999913},
   file = {/home/nathante/Zotero/storage/TVUYGPSE/Dobbrick et al_2021_Enhancing Theory-Informed Dictionary Approaches with “Glass-box” Machine.pdf}
 }
 
@@ -580,7 +571,6 @@
   pages = {395--419},
   abstract = {Social scientists are now in an era of data abundance, and machine learning tools are increasingly used to extract meaning from data sets both massive and small. We explain how the inclusion of machine learning in the social sciences requires us to rethink not only applications of machine learning methods but also best practices in the social sciences. In contrast to the traditional tasks for machine learning in computer science and statistics, when machine learning is applied to social scientific data, it is used to discover new concepts, measure the prevalence of those concepts, assess causal effects, and make predictions. The abundance of data and resources facilitates the move away from a deductive social science to a more sequential, interactive, and ultimately inductive approach to inference. We explain how an agnostic approach to machine learning methods focused on the social science tasks facilitates progress across a wide range of questions.},
   keywords = {machine learning,research design,text as data},
-  annotation = {\_eprint: https://doi.org/10.1146/annurev-polisci-053119-015921},
   file = {/home/nathante/Zotero/storage/N4PR8YCM/Grimmer et al_2021_Machine Learning for Social Science.pdf}
 }
 
@@ -680,7 +670,6 @@
   issn = {2044-8317},
   abstract = {Pi (π) and kappa (κ) statistics are widely used in the areas of psychiatry and psychological testing to compute the extent of agreement between raters on nominally scaled data. It is a fact that these coefficients occasionally yield unexpected results in situations known as the paradoxes of kappa. This paper explores the origin of these limitations, and introduces an alternative and more stable agreement coefficient referred to as the AC1 coefficient. Also proposed are new variance estimators for the multiple-rater generalized π and AC1 statistics, whose validity does not depend upon the hypothesis of independence between raters. This is an improvement over existing alternative variances, which depend on the independence assumption. A Monte-Carlo simulation study demonstrates the validity of these variance estimators for confidence interval construction, and confirms the value of AC1 as an improved alternative to existing inter-rater reliability statistics.},
   langid = {english},
-  annotation = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1348/000711006X126600},
   file = {/home/nathante/Zotero/storage/2Y58TMMP/000711006X126600.html}
 }
 
@@ -770,8 +759,7 @@
   pages = {77--89},
   publisher = {{Routledge}},
   issn = {1931-2458},
-  abstract = {In content analysis and similar methods, data are typically generated by trained human observers who record or transcribe textual, pictorial, or audible matter in terms suitable for analysis. Conclusions from such data can be trusted only after demonstrating their reliability. Unfortunately, the content analysis literature is full of proposals for so-called reliability coefficients, leaving investigators easily confused, not knowing which to choose. After describing the criteria for a good measure of reliability, we propose Krippendorff's alpha as the standard reliability measure. It is general in that it can be used regardless of the number of observers, levels of measurement, sample sizes, and presence or absence of missing data. To facilitate the adoption of this recommendation, we describe a freely available macro written for SPSS and SAS to calculate Krippendorff's alpha and illustrate its use with a simple example.},
+  abstract = {In content analysis and similar methods, data are typically generated by trained human observers who record or transcribe textual, pictorial, or audible matter in terms suitable for analysis. Conclusions from such data can be trusted only after demonstrating their reliability. Unfortunately, the content analysis literature is full of proposals for so-called reliability coefficients, leaving investigators easily confused, not knowing which to choose. After describing the criteria for a good measure of reliability, we propose Krippendorff's alpha as the standard reliability measure. It is general in that it can be used regardless of the number of observers, levels of measurement, sample sizes, and presence or absence of missing data. To facilitate the adoption of this recommendation, we describe a freely available macro written for SPSS and SAS to calculate Krippendorff's alpha and illustrate its use with a simple example.}
-  annotation = {\_eprint: https://doi.org/10.1080/19312450709336664}
 }
 
 @inproceedings{hede_toxicity_2021,
@@ -858,17 +846,15 @@
   file = {/home/nathante/Zotero/storage/UREW3WG6/Hopp_Vargo_2019_Social Capital as an Inhibitor of Online Political Incivility.pdf}
 }
 
-@misc{hosseini_deceiving_2017,
+@online{hosseini_deceiving_2017,
   title = {Deceiving {{Google}}'s {{Perspective API Built}} for {{Detecting Toxic Comments}}},
   author = {Hosseini, Hossein and Kannan, Sreeram and Zhang, Baosen and Poovendran, Radha},
   date = {2017-02-26},
   number = {arXiv:1702.08138},
-  eprint = {1702.08138},
+  eprint = {arXiv:1702.08138},
   eprinttype = {arxiv},
-  primaryclass = {cs},
-  publisher = {{arXiv}},
   abstract = {Social media platforms provide an environment where people can freely engage in discussions. Unfortunately, they also enable several problems, such as online harassment. Recently, Google and Jigsaw started a project called Perspective, which uses machine learning to automatically detect toxic language. A demonstration website has been also launched, which allows anyone to type a phrase in the interface and instantaneously see the toxicity score [1]. In this paper, we propose an attack on the Perspective toxic detection system based on the adversarial examples. We show that an adversary can subtly modify a highly toxic phrase in a way that the system assigns significantly lower toxicity score to it. We apply the attack on the sample phrases provided in the Perspective website and show that we can consistently reduce the toxicity scores to the level of the non-toxic phrases. The existence of such adversarial examples is very harmful for toxic detection systems and seriously undermines their usability.},
-  archiveprefix = {arXiv},
+  pubstate = {preprint},
   keywords = {Computer Science - Computers and Society,Computer Science - Machine Learning,Computer Science - Social and Information Networks},
   file = {/home/nathante/Zotero/storage/7DNERYPW/Hosseini et al_2017_Deceiving Google's Perspective API Built for Detecting Toxic Comments.pdf;/home/nathante/Zotero/storage/AJM3CAWA/1702.html}
 }
@@ -1002,7 +988,6 @@
   abstract = {A number of commentaries have suggested that large studies are more reliable than smaller studies and there is a growing interest in the analysis of “big data” that integrates information from many thousands of persons and/or different data sources. We consider a variety of biases that are likely in the era of big data, including sampling error, measurement error, multiple comparisons errors, aggregation error, and errors associated with the systematic exclusion of information. Using examples from epidemiology, health services research, studies on determinants of health, and clinical trials, we conclude that it is necessary to exercise greater caution to be sure that big sample size does not lead to big inferential errors. Despite the advantages of big studies, large sample size can magnify the bias associated with error resulting from sampling or study design. Clin Trans Sci 2014; Volume \#: 1–5},
   langid = {english},
   keywords = {bias,big data,research methods,sampling},
-  annotation = {\_eprint: https://ascpt.onlinelibrary.wiley.com/doi/pdf/10.1111/cts.12178},
   file = {/home/nathante/Zotero/storage/PTGVP2WW/Kaplan et al_2014_Big Data and Large Sample Size.pdf;/home/nathante/Zotero/storage/KBURTV5N/cts.html}
 }
 
@@ -1099,15 +1084,13 @@
   issn = {1468-2958},
   abstract = {In a recent article in this journal, Lombard, Snyder-Duch, and Bracken (2002) surveyed 200 content analyses for their reporting of reliability tests, compared the virtues and drawbacks of five popular reliability measures, and proposed guidelines and standards for their use. Their discussion revealed that numerous misconceptions circulate in the content analysis literature regarding how these measures behave and can aid or deceive content analysts in their effort to ensure the reliability of their data. This article proposes three conditions for statistical measures to serve as indices of the reliability of data and examines the mathematical structure and the behavior of the five coefficients discussed by the authors, as well as two others. It compares common beliefs about these coefficients with what they actually do and concludes with alternative recommendations for testing reliability in content analysis and similar data-making efforts.},
   langid = {english},
-  annotation = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1468-2958.2004.tb00738.x},
   file = {/home/nathante/Zotero/storage/GBK9844Z/j.1468-2958.2004.tb00738.html}
 }
 
 @article{kroon_beyond_2022,
   title = {Beyond {{Counting Words}}: {{Assessing Performance}} of {{Dictionaries}}, {{Supervised Machine Learning}}, and {{Embeddings}} in {{Topic}} and {{Frame Classification}}},
   shorttitle = {Beyond {{Counting Words}}},
-  author = {Kroon, Anne C. and van der Meer, Toni and Vliegenthart, Rens},
+  author = {Kroon, Anne C. and family=Meer, given=Toni, prefix=van der, useprefix=true and Vliegenthart, Rens},
-  options = {useprefix=true},
   date = {2022-10-01},
   journaltitle = {Computational Communication Research},
   volume = {4},
@@ -1247,8 +1230,7 @@
 
 @article{mahl_noise_2022,
   title = {Noise {{Pollution}}: {{A Multi-Step Approach}} to {{Assessing}} the {{Consequences}} of ({{Not}}) {{Validating Search Terms}} on {{Automated Content Analyses}}},
-  author = {Mahl, Daniela and von Nordheim, Gerret and Guenther, Lars},
+  author = {Mahl, Daniela and family=Nordheim, given=Gerret, prefix=von, useprefix=true and Guenther, Lars},
-  options = {useprefix=true},
   date = {2022-09-23},
   journaltitle = {Digital Journalism},
   shortjournal = {Digital Journalism},
@@ -1352,6 +1334,35 @@
   file = {/home/nathante/Zotero/storage/2W869JT8/Millimet und Parmeter - 2022 - Accounting for Skewed or One-Sided Measurement Err.pdf}
 }
 
+@inproceedings{mitchell_model_2019,
+  title = {Model {{Cards}} for {{Model Reporting}}},
+  booktitle = {Proceedings of the {{Conference}} on {{Fairness}}, {{Accountability}}, and {{Transparency}}},
+  author = {Mitchell, Margaret and Wu, Simone and Zaldivar, Andrew and Barnes, Parker and Vasserman, Lucy and Hutchinson, Ben and Spitzer, Elena and Raji, Inioluwa Deborah and Gebru, Timnit},
+  date = {2019-01-29},
+  pages = {220--229},
+  publisher = {{ACM}},
+  location = {{Atlanta GA USA}},
+  eventtitle = {{{FAT}}* '19: {{Conference}} on {{Fairness}}, {{Accountability}}, and {{Transparency}}},
+  isbn = {978-1-4503-6125-5},
+  langid = {english},
+  file = {/home/nathante/Zotero/storage/ZHZ9CP8M/Mitchell et al. - 2019 - Model Cards for Model Reporting.pdf}
+}
+
+@inproceedings{mitchell_model_2019-1,
+  title = {Model {{Cards}} for {{Model Reporting}}},
+  booktitle = {Proceedings of the {{Conference}} on {{Fairness}}, {{Accountability}}, and {{Transparency}}},
+  author = {Mitchell, Margaret and Wu, Simone and Zaldivar, Andrew and Barnes, Parker and Vasserman, Lucy and Hutchinson, Ben and Spitzer, Elena and Raji, Inioluwa Deborah and Gebru, Timnit},
+  date = {2019-01-29},
+  series = {{{FAT}}* '19},
+  pages = {220--229},
+  publisher = {{Association for Computing Machinery}},
+  location = {{New York, NY, USA}},
+  abstract = {Trained machine learning models are increasingly used to perform high-impact tasks in areas such as law enforcement, medicine, education, and employment. In order to clarify the intended use cases of machine learning models and minimize their usage in contexts for which they are not well suited, we recommend that released models be accompanied by documentation detailing their performance characteristics. In this paper, we propose a framework that we call model cards, to encourage such transparent model reporting. Model cards are short documents accompanying trained machine learning models that provide benchmarked evaluation in a variety of conditions, such as across different cultural, demographic, or phenotypic groups (e.g., race, geographic location, sex, Fitzpatrick skin type [15]) and intersectional groups (e.g., age and race, or sex and Fitzpatrick skin type) that are relevant to the intended application domains. Model cards also disclose the context in which models are intended to be used, details of the performance evaluation procedures, and other relevant information. While we focus primarily on human-centered machine learning models in the application fields of computer vision and natural language processing, this framework can be used to document any trained machine learning model. To solidify the concept, we provide cards for two supervised models: One trained to detect smiling faces in images, and one trained to detect toxic comments in text. We propose model cards as a step towards the responsible democratization of machine learning and related artificial intelligence technology, increasing transparency into how well artificial intelligence technology works. We hope this work encourages those releasing trained machine learning models to accompany model releases with similar detailed evaluation numbers and other relevant documentation.},
+  isbn = {978-1-4503-6125-5},
+  keywords = {datasheets,disaggregated evaluation,documentation,ethical considerations,fairness evaluation,ML model evaluation,model cards},
+  file = {/home/nathante/Zotero/storage/4T2GRQ6M/Mitchell et al_2019_Model Cards for Model Reporting.pdf}
+}
+
 @article{mittos_and_2020,
   title = {“{{And We Will Fight}} for {{Our Race}}!” {{A Measurement Study}} of {{Genetic Testing Conversations}} on {{Reddit}} and 4chan},
   author = {Mittos, Alexandros and Zannettou, Savvas and Blackburn, Jeremy and Cristofaro, Emiliano De},
@@ -1394,7 +1405,6 @@
   issn = {1058-4609},
   abstract = {Content analysis of large-scale textual data sets poses myriad problems, particularly when researchers seek to analyze content that is both theoretically derived and context dependent. In this piece, we detail the approach we developed to tackle the analysis of the context-dependent content of political incivility. After describing our manually validated organic dictionaries approach, we compare the method to others we could have used and then replicate the method in a different—but still context-dependent—project examining political issue content on social media. We conclude by summarizing the strengths and weaknesses of the approach and offering suggestions for future research that can refine and expand the method.},
   keywords = {computer-aided content analysis,incivility,news comments,news issues,Twitter},
-  annotation = {\_eprint: https://doi.org/10.1080/10584609.2018.1517843},
   file = {/home/nathante/Zotero/storage/MKDWDL4K/Muddiman et al_2019_(Re)Claiming Our Expertise.pdf}
 }
 
@@ -1413,8 +1423,7 @@
 @article{nab_mecor_2021,
   title = {Mecor: {{An R}} Package for Measurement Error Correction in Linear Regression Models with a Continuous Outcome},
   shorttitle = {Mecor},
-  author = {Nab, Linda and van Smeden, Maarten and Keogh, Ruth H. and Groenwold, Rolf H. H.},
+  author = {Nab, Linda and family=Smeden, given=Maarten, prefix=van, useprefix=true and Keogh, Ruth H. and Groenwold, Rolf H. H.},
-  options = {useprefix=true},
   date = {2021-09-01},
   journaltitle = {Computer Methods and Programs in Biomedicine},
   shortjournal = {Computer Methods and Programs in Biomedicine},
@@ -1430,8 +1439,7 @@
 @article{nab_quantitative_2020,
   title = {Quantitative {{Bias Analysis}} for a {{Misclassified Confounder}}: {{A Comparison Between Marginal Structural Models}} and {{Conditional Models}} for {{Point Treatments}}},
   shorttitle = {Quantitative {{Bias Analysis}} for a {{Misclassified Confounder}}},
-  author = {Nab, Linda and Groenwold, Rolf H. H. and van Smeden, Maarten and Keogh, Ruth H.},
+  author = {Nab, Linda and Groenwold, Rolf H. H. and family=Smeden, given=Maarten, prefix=van, useprefix=true and Keogh, Ruth H.},
-  options = {useprefix=true},
   date = {2020-11},
   journaltitle = {Epidemiology},
   volume = {31},
@@ -1443,9 +1451,10 @@
   file = {/home/nathante/Zotero/storage/TIKY8Z49/Nab et al_2020_Quantitative Bias Analysis for a Misclassified Confounder.pdf;/home/nathante/Zotero/storage/YPZQ4NGF/Quantitative_Bias_Analysis_for_a_Misclassified.7.html}
 }
 
-@misc{nicholls_deep_nodate,
+@online{nicholls_deep_nodate,
   title = {Deep Learning Models for Multilingual Supervised Political Text Classification},
-  author = {Nicholls, Thomas and Culpepper, Pepper D}
+  author = {Nicholls, Thomas and Culpepper, Pepper D},
+  pubstate = {preprint}
 }
 
 @online{noauthor_jigsaw_nodate,
@@ -1585,7 +1594,6 @@
   publisher = {{Routledge}},
   issn = {1931-2458},
   abstract = {The goal of this research is to make progress towards using supervised machine learning for automated content analysis dealing with complex interpretations of text. For Step 1, two humans coded a sub-sample of online forum posts for relational uncertainty. For Step 2, we evaluated reliability, in which we trained three different classifiers to learn from those subjective human interpretations. Reliability was established when two different metrics of inter-coder reliability could not distinguish whether a human or a machine coded the text on a separate hold-out set. Finally, in Step 3 we assessed validity. To accomplish this, we administered a survey in which participants described their own relational uncertainty/certainty via text and completed a questionnaire. After classifying the text, the machine’s classifications of the participants’ text positively correlated with the subjects’ own self-reported relational uncertainty and relational satisfaction. We discuss our results in line with areas of computational communication science, content analysis, and interpersonal communication.},
-  annotation = {\_eprint: https://doi.org/10.1080/19312458.2019.1650166},
   file = {/home/nathante/Zotero/storage/6W4S82UP/Pilny et al_2019_Using Supervised Machine Learning in Automated Content Analysis.pdf;/home/nathante/Zotero/storage/VZHKQWIE/19312458.2019.html}
 }
 
@@ -1676,7 +1684,6 @@
   issn = {1467-9299},
   abstract = {The Institutional Grammar (IG) is used to analyse the syntactic structure of statements constituting institutions (e.g., policies, regulations, and norms) that indicate behavioural constraints and parameterize features of institutionally governed domains. Policy and administration scholars have made considerable progress in methodologically developing the IG, offering increasingly clear guidelines for IG-based coding, identifying unique considerations for applying the IG to different types of institutions, and expanding its syntactic scope. However, while validated as a robust institutional analysis approach, the resource and time commitment associated with its application has precipitated concerns over whether the IG might ever enjoy widespread use. Needed now in the methodological development of the IG are reliable and accessible (i.e., open source) approaches that reduce the costs associated with its application. We propose an automated approach leveraging computational text analysis and natural language processing. We then present results from an evaluation in the context of food system regulations.},
   langid = {english},
-  annotation = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/padm.12711},
   file = {/home/nathante/Zotero/storage/C7ZBPYPY/padm.html}
 }
 
@@ -1752,7 +1759,6 @@
   issn = {1058-4609},
   abstract = {In the debate on minimal media effects and their causes, methodological concerns about measurement are rarely discussed. We argue that even in state-of-the-art media-effects studies that combine measures of media messages and media use (i.e., linkage analyses), measurement error in both the media content analysis and the media use self-reports will typically lead to severely downward-biased effect estimates. We demonstrate this phenomenon using a large Monte Carlo simulation with varying parameters of the content analysis and the survey study. Results show that measurement error in the content analysis and media use variables does indeed lead to smaller effect estimates, especially when the media messages of interest are relatively rare. We discuss these findings as well as possible remedies and implications for future research.},
   keywords = {content analysis,Corrigendum,linkage analysis,media effects,media use,Monte Carlo simulation,reliability},
-  annotation = {\_eprint: https://doi.org/10.1080/10584609.2016.1235640},
   file = {/home/nathante/Zotero/storage/M5A6LIZQ/Scharkow_Bachl_2017_How Measurement Error in Content Analysis and Self-Reported Media Use Leads to.pdf}
 }
 
@@ -1867,7 +1873,6 @@
   eprinttype = {arxiv},
   pages = {56:1--56:27},
   abstract = {Online community moderators often rely on social signals such as whether or not a user has an account or a profile page as clues that users may cause problems. Reliance on these clues can lead to "overprofiling'' bias when moderators focus on these signals but overlook the misbehavior of others. We propose that algorithmic flagging systems deployed to improve the efficiency of moderation work can also make moderation actions more fair to these users by reducing reliance on social signals and making norm violations by everyone else more visible. We analyze moderator behavior in Wikipedia as mediated by RCFilters, a system which displays social signals and algorithmic flags, and estimate the causal effect of being flagged on moderator actions. We show that algorithmically flagged edits are reverted more often, especially those by established editors with positive social signals, and that flagging decreases the likelihood that moderation actions will be undone. Our results suggest that algorithmic flagging systems can lead to increased fairness in some contexts but that the relationship is complex and contingent.},
-  archiveprefix = {arXiv},
   issue = {CSCW1},
   keywords = {ai,causal inference,community norms,fairness,machine learning,moderation,online communities,peer production,sociotechnical systems,wikipedia},
   file = {/home/nathante/Zotero/storage/8KVI8QKZ/TeBlunthuis et al. - 2021 - Effects of Algorithmic Flagging on Fairness Quasi.pdf;/home/nathante/Zotero/storage/E2RPTEMM/TeBlunthuis et al_2021_Effects of Algorithmic Flagging on Fairness.pdf;/home/nathante/Zotero/storage/LAJEZ9JV/TeBlunthuis et al. - 2021 - Effects of Algorithmic Flagging on Fairness Quasi.pdf;/home/nathante/Zotero/storage/NWM56G48/TeBlunthuis et al_2020_The effects of algorithmic flagging on fairness.pdf;/home/nathante/Zotero/storage/YBYI7VSP/2006.html}
@@ -1914,15 +1919,13 @@
   publisher = {{Routledge}},
   issn = {1931-2458},
   abstract = {Employing a number of different standalone programs is a prevalent approach among communication scholars who use computational methods to analyze media content. For instance, a researcher might use a specific program or a paid service to scrape some content from the Web, then use another program to process the resulting data, and finally conduct statistical analysis or produce some visualizations in yet another program. This makes it hard to build reproducible workflows, and even harder to build on the work of earlier studies. To improve this situation, we propose and discuss four criteria that a framework for automated content analysis should fulfill: scalability, free and open source, adaptability, and accessibility via multiple interfaces. We also describe how to put these considerations into practice, discuss their feasibility, and point toward future developments.},
-  annotation = {\_eprint: https://doi.org/10.1080/19312458.2018.1447655},
   file = {/home/nathante/Zotero/storage/8EAAYQQE/Trilling_Jonkman_2018_Scaling up Content Analysis.pdf}
 }
 
 @article{van_atteveldt_validity_2021,
   title = {The {{Validity}} of {{Sentiment Analysis}}: {{Comparing Manual Annotation}}, {{Crowd-Coding}}, {{Dictionary Approaches}}, and {{Machine Learning Algorithms}}},
   shorttitle = {The {{Validity}} of {{Sentiment Analysis}}},
-  author = {van Atteveldt, Wouter and van der Velden, Mariken A. C. G. and Boukes, Mark},
+  author = {family=Atteveldt, given=Wouter, prefix=van, useprefix=true and family=Velden, given=Mariken A. C. G., prefix=van der, useprefix=true and Boukes, Mark},
-  options = {useprefix=true},
   date = {2021-04-03},
   journaltitle = {Communication Methods and Measures},
   volume = {15},
@@ -1937,8 +1940,7 @@
 @article{van_smeden_reflection_2020,
   title = {Reflection on Modern Methods: Five Myths about Measurement Error in Epidemiological Research},
   shorttitle = {Reflection on Modern Methods},
-  author = {van Smeden, Maarten and Lash, Timothy L and Groenwold, Rolf H H},
+  author = {family=Smeden, given=Maarten, prefix=van, useprefix=true and Lash, Timothy L and Groenwold, Rolf H H},
-  options = {useprefix=true},
   date = {2020-02-01},
   journaltitle = {International Journal of Epidemiology},
   shortjournal = {International Journal of Epidemiology},
@@ -1953,8 +1955,7 @@
 @article{vermeer_online_2020,
   title = {Online {{News User Journeys}}: {{The Role}} of {{Social Media}}, {{News Websites}}, and {{Topics}}},
   shorttitle = {Online {{News User Journeys}}},
-  author = {Vermeer, Susan and Trilling, Damian and Kruikemeier, Sanne and de Vreese, Claes},
+  author = {Vermeer, Susan and Trilling, Damian and Kruikemeier, Sanne and family=Vreese, given=Claes, prefix=de, useprefix=true},
-  options = {useprefix=true},
   date = {2020-10-20},
   journaltitle = {Digital Journalism},
   shortjournal = {Digital Journalism},
@@ -1969,8 +1970,7 @@
 @article{votta_going_2023,
   title = {Going {{Micro}} to {{Go Negative}}?: {{Targeting Toxicity}} Using {{Facebook}} and {{Instagram Ads}}},
   shorttitle = {Going {{Micro}} to {{Go Negative}}?},
-  author = {Votta, Fabio and Noroozian, Arman and Dobber, Tom and Helberger, Natali and de Vreese, Claes},
+  author = {Votta, Fabio and Noroozian, Arman and Dobber, Tom and Helberger, Natali and family=Vreese, given=Claes, prefix=de, useprefix=true},
-  options = {useprefix=true},
   date = {2023-02-01},
   journaltitle = {Computational Communication Research},
   volume = {5},
@@ -2002,8 +2002,7 @@
   pages = {119--139},
   publisher = {{Routledge}},
   issn = {1931-2458},
-  abstract = {Moral Foundations Theory (MFT) and the Model of Intuitive Morality and Exemplars (MIME) contend that moral judgments are built on a universal set of basic moral intuitions. A large body of research has supported many of MFT’s and the MIME’s central hypotheses. Yet, an important prerequisite of this research—the ability to extract latent moral content represented in media stimuli with a reliable procedure—has not been systematically studied. In this article, we subject different extraction procedures to rigorous tests, underscore challenges by identifying a range of reliabilities, develop new reliability test and coding procedures employing computational methods, and provide solutions that maximize the reliability and validity of moral intuition extraction. In six content analytical studies, including a large crowd-based study, we demonstrate that: (1) traditional content analytical approaches lead to rather low reliabilities; (2) variation in coding reliabilities can be predicted by both text features and characteristics of the human coders; and (3) reliability is largely unaffected by the detail of coder training. We show that a coding task with simplified training and a coding technique that treats moral foundations as fast, spontaneous intuitions leads to acceptable inter-rater agreement, and potentially to more valid moral intuition extractions. While this study was motivated by issues related to MFT and MIME research, the methods and findings in this study have implications for extracting latent content from text narratives that go beyond moral information. Accordingly, we provide a tool for researchers interested in applying this new approach in their own work.},
+  abstract = {Moral Foundations Theory (MFT) and the Model of Intuitive Morality and Exemplars (MIME) contend that moral judgments are built on a universal set of basic moral intuitions. A large body of research has supported many of MFT’s and the MIME’s central hypotheses. Yet, an important prerequisite of this research—the ability to extract latent moral content represented in media stimuli with a reliable procedure—has not been systematically studied. In this article, we subject different extraction procedures to rigorous tests, underscore challenges by identifying a range of reliabilities, develop new reliability test and coding procedures employing computational methods, and provide solutions that maximize the reliability and validity of moral intuition extraction. In six content analytical studies, including a large crowd-based study, we demonstrate that: (1) traditional content analytical approaches lead to rather low reliabilities; (2) variation in coding reliabilities can be predicted by both text features and characteristics of the human coders; and (3) reliability is largely unaffected by the detail of coder training. We show that a coding task with simplified training and a coding technique that treats moral foundations as fast, spontaneous intuitions leads to acceptable inter-rater agreement, and potentially to more valid moral intuition extractions. While this study was motivated by issues related to MFT and MIME research, the methods and findings in this study have implications for extracting latent content from text narratives that go beyond moral information. Accordingly, we provide a tool for researchers interested in applying this new approach in their own work.}
-  annotation = {\_eprint: https://doi.org/10.1080/19312458.2018.1447656}
 }
 
 @article{weld_adjusting_2022,
@@ -2111,6 +2110,5 @@
   pages = {419--480},
   publisher = {{Routledge}},
   issn = {2380-8985},
-  annotation = {\_eprint: https://doi.org/10.1080/23808985.2013.11679142},
   file = {/home/nathante/Zotero/storage/TDF2I55Y/Zhao et al_2013_Assumptions behind Intercoder Reliability Indices.pdf;/home/nathante/Zotero/storage/64NWAITD/23808985.2013.html}
 }