Pub Date : 2023-03-06DOI: 10.1080/19312458.2023.2183188
Kilian Buehling
{"title":"Message Deletion on Telegram: Affected Data Types and Implications for Computational Analysis","authors":"Kilian Buehling","doi":"10.1080/19312458.2023.2183188","DOIUrl":"https://doi.org/10.1080/19312458.2023.2183188","url":null,"abstract":"","PeriodicalId":47552,"journal":{"name":"Communication Methods and Measures","volume":null,"pages":null},"PeriodicalIF":11.4,"publicationDate":"2023-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47638390","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"文学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-01DOI: 10.1080/19312458.2023.2176474
Nico Pfiffner, Thomas N. Friemel
ABSTRACT Using data donations to collect digital trace data holds great potential for communication research, which has not yet been fully realized. Besides limited awareness and expertise among researchers, a central challenge is to motivate people to donate their personal data. Therefore, this article investigates which factors affect people’s willingness to donate across different platforms and data types. The study applies a multilevel approach that explains the reported willingness to donate different types of data (level 1) belonging to different platforms (level 2) from potential data donors with individual characteristics (level 3) to a hypothetical research project. The analysis is based on data collected through a national online survey (n = 833). We find higher willingness to donate YouTube data compared to Facebook, Instagram, or Google, as well as relevant influencing factors at all three levels. Greater willingness is found for lower perceived sensitivity and higher perceived relevance of the data (level of data type), greater perceived behavioral control to request and submit the data (platform level), more favorable attitudes toward data donation and the donation purpose, as well as lower contextual privacy concerns (individual level). Based on these findings, practical implications for future data donation studies are proposed.
{"title":"Leveraging Data Donations for Communication Research: Exploring Drivers Behind the Willingness to Donate","authors":"Nico Pfiffner, Thomas N. Friemel","doi":"10.1080/19312458.2023.2176474","DOIUrl":"https://doi.org/10.1080/19312458.2023.2176474","url":null,"abstract":"ABSTRACT Using data donations to collect digital trace data holds great potential for communication research, which has not yet been fully realized. Besides limited awareness and expertise among researchers, a central challenge is to motivate people to donate their personal data. Therefore, this article investigates which factors affect people’s willingness to donate across different platforms and data types. The study applies a multilevel approach that explains the reported willingness to donate different types of data (level 1) belonging to different platforms (level 2) from potential data donors with individual characteristics (level 3) to a hypothetical research project. The analysis is based on data collected through a national online survey (n = 833). We find higher willingness to donate YouTube data compared to Facebook, Instagram, or Google, as well as relevant influencing factors at all three levels. Greater willingness is found for lower perceived sensitivity and higher perceived relevance of the data (level of data type), greater perceived behavioral control to request and submit the data (platform level), more favorable attitudes toward data donation and the donation purpose, as well as lower contextual privacy concerns (individual level). Based on these findings, practical implications for future data donation studies are proposed.","PeriodicalId":47552,"journal":{"name":"Communication Methods and Measures","volume":null,"pages":null},"PeriodicalIF":11.4,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47124976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"文学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-27DOI: 10.1080/19312458.2023.2181319
Jakob Ohme, Theo Araujo, L. Boeschoten, Deen Freelon, Nilam Ram, Byron B. Reeves, Thomas N. Robinson
{"title":"Digital Trace Data Collection for Social Media Effects Research: APIs, Data Donation, and (Screen) Tracking","authors":"Jakob Ohme, Theo Araujo, L. Boeschoten, Deen Freelon, Nilam Ram, Byron B. Reeves, Thomas N. Robinson","doi":"10.1080/19312458.2023.2181319","DOIUrl":"https://doi.org/10.1080/19312458.2023.2181319","url":null,"abstract":"","PeriodicalId":47552,"journal":{"name":"Communication Methods and Measures","volume":null,"pages":null},"PeriodicalIF":11.4,"publicationDate":"2023-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42634187","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"文学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-23DOI: 10.1080/19312458.2023.2179612
Colton E. Krawietz, Rudy C. Pett
ABSTRACT Recently, latent variable mixture modeling has gained traction in many disciplines, given its unique ability to discover unknown groups within a broader population. Indeed, this method assumes that a finite number of mixtures (i.e. unknown groups) exist within the population and can be discovered by evaluating participants’ response patterns to a set of manifest indicators. Despite the intuitive approach, recommendations have been proposed to overcome some methodological concerns associated with latent variable mixture modeling. The primary purpose of this study was to understand the characteristics of latent variable mixture modeling in communication research and to evaluate the extent to which the existing research meets these recommendations. Ninety-five manuscripts published between 2010 and 2022 in 18 communication journals were identified and systematically analyzed. The review found that (1) the use of latent variable mixture modeling has increased; (2) latent class analysis and latent profile analysis are the most common models; and (3) most manuscripts did not meet the proscribed standards for random start values, auxiliary variable procedures, indicator requirements, and missing data procedures. These findings are discussed more in comparison with the proscribed standards. In addition, conceptual and applicable recommendations are provided to improve communication scholarship.
{"title":"A Systematic Literature Review of Latent Variable Mixture Modeling in Communication Scholarship","authors":"Colton E. Krawietz, Rudy C. Pett","doi":"10.1080/19312458.2023.2179612","DOIUrl":"https://doi.org/10.1080/19312458.2023.2179612","url":null,"abstract":"ABSTRACT Recently, latent variable mixture modeling has gained traction in many disciplines, given its unique ability to discover unknown groups within a broader population. Indeed, this method assumes that a finite number of mixtures (i.e. unknown groups) exist within the population and can be discovered by evaluating participants’ response patterns to a set of manifest indicators. Despite the intuitive approach, recommendations have been proposed to overcome some methodological concerns associated with latent variable mixture modeling. The primary purpose of this study was to understand the characteristics of latent variable mixture modeling in communication research and to evaluate the extent to which the existing research meets these recommendations. Ninety-five manuscripts published between 2010 and 2022 in 18 communication journals were identified and systematically analyzed. The review found that (1) the use of latent variable mixture modeling has increased; (2) latent class analysis and latent profile analysis are the most common models; and (3) most manuscripts did not meet the proscribed standards for random start values, auxiliary variable procedures, indicator requirements, and missing data procedures. These findings are discussed more in comparison with the proscribed standards. In addition, conceptual and applicable recommendations are provided to improve communication scholarship.","PeriodicalId":47552,"journal":{"name":"Communication Methods and Measures","volume":null,"pages":null},"PeriodicalIF":11.4,"publicationDate":"2023-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42567266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"文学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-23DOI: 10.1080/19312458.2023.2182983
Published in Communication Methods and Measures (Vol. 17, No. 2, 2023)
《通信方法与措施》(第17卷第2期，2023年)
{"title":"Correction","authors":"","doi":"10.1080/19312458.2023.2182983","DOIUrl":"https://doi.org/10.1080/19312458.2023.2182983","url":null,"abstract":"Published in Communication Methods and Measures (Vol. 17, No. 2, 2023)","PeriodicalId":47552,"journal":{"name":"Communication Methods and Measures","volume":null,"pages":null},"PeriodicalIF":11.4,"publicationDate":"2023-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50165780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"文学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-22DOI: 10.1080/19312458.2023.2182278
Dror K. Markus, Guy Mor-Lan, Tamir Sheafer, Shaul R. Shenhav
ABSTRACT As more computational communication researchers turn to supervised machine learning methods for text classification, we note the challenge in implementing such techniques within an imbalanced dataset. Such issues are critical in our domain, where, in many cases, researchers attempt to identify and study theoretically interesting categories that can be rare in a target corpus. Specifically, imbalanced distributions, with a skewed distribution of texts among the categories, can lead to a lengthy and expensive annotation stage, forcing practitioners to sample and label large numbers of texts to train a classification model. In this paper, we provide an overview of the issue, and describe existing strategies for mitigating such challenges. Noting the pitfalls of previous solutions, we then provide a semi-supervised method – Expert Initiated Latent Space Sampling – that complements researcher domain expertise with a systematic, unsupervised exploration of the latent semantic space to overcome such limitations. Utilizing simulations to systematically evaluate our method and compare it to existing approaches, we show that our procedure offers significant advantages in terms of efficiency and accuracy in many classification tasks.
{"title":"Leveraging Researcher Domain Expertise to Annotate Concepts Within Imbalanced Data","authors":"Dror K. Markus, Guy Mor-Lan, Tamir Sheafer, Shaul R. Shenhav","doi":"10.1080/19312458.2023.2182278","DOIUrl":"https://doi.org/10.1080/19312458.2023.2182278","url":null,"abstract":"ABSTRACT As more computational communication researchers turn to supervised machine learning methods for text classification, we note the challenge in implementing such techniques within an imbalanced dataset. Such issues are critical in our domain, where, in many cases, researchers attempt to identify and study theoretically interesting categories that can be rare in a target corpus. Specifically, imbalanced distributions, with a skewed distribution of texts among the categories, can lead to a lengthy and expensive annotation stage, forcing practitioners to sample and label large numbers of texts to train a classification model. In this paper, we provide an overview of the issue, and describe existing strategies for mitigating such challenges. Noting the pitfalls of previous solutions, we then provide a semi-supervised method – Expert Initiated Latent Space Sampling – that complements researcher domain expertise with a systematic, unsupervised exploration of the latent semantic space to overcome such limitations. Utilizing simulations to systematically evaluate our method and compare it to existing approaches, we show that our procedure offers significant advantages in terms of efficiency and accuracy in many classification tasks.","PeriodicalId":47552,"journal":{"name":"Communication Methods and Measures","volume":null,"pages":null},"PeriodicalIF":11.4,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44019352","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"文学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-05DOI: 10.1080/19312458.2023.2166028
Anke Stoll, L. Wilms, Marc Ziegele
ABSTRACT Incivility in online discussions has become an important issue in political communication research. Instruments and tools for the automated analysis of uncivil content, however, are rare, especially for non-English user-generated text. In this study, we present a) an extensive dictionary (DIKI - Diktionär für Inzivilität, English: Dictionary for Incivility) to detect incivility in German-language online discussions, and b) a semi-automated two-step-approach that combines manual content analysis with automated keyword collection using a pre-trained word embedding model. We show that DIKI clearly outperforms comparable dictionaries that have been used as alternative instruments to measure incivility (e.g., the LIWC) as well as basic machine learning approaches to text classification. Further, we provide evidence that pre-trained word embeddings can fruitfully be employed in the explorative phase of creating dictionaries. Still, the manual evaluation of DIKI confirms that detecting complex and context-dependent forms of incivility remains challenging and constant update would be needed to maintain performance. Finally, the detailed documentation of the developing and evaluation process of DIKI may serve as a guideline for further research. We therefore provide DIKI as a freely available instrument that also will be applicable in a web interface for drag-and-drop data analysis (diki.limitedminds.org).
网络讨论中的不文明行为已经成为政治传播研究中的一个重要问题。然而，用于自动分析不文明内容的仪器和工具很少，特别是对于非英语用户生成的文本。在这项研究中，我们提出了a)一个广泛的词典(DIKI - Diktionär f r Inzivilität，英语:dictionary for Incivility)来检测德语在线讨论中的不文明行为，以及b)一种半自动的两步方法，该方法将人工内容分析与使用预训练词嵌入模型的自动关键字收集相结合。我们表明，DIKI明显优于可比较的词典，这些词典已被用作衡量不文明的替代工具(例如，LIWC)，以及用于文本分类的基本机器学习方法。此外，我们提供的证据表明，预训练的词嵌入可以有效地用于创建字典的探索阶段。尽管如此，DIKI的人工评估证实，检测复杂和依赖于上下文的不文明形式仍然具有挑战性，需要不断更新以保持性能。最后，对DIKI的发展和评价过程的详细记录可以作为进一步研究的指导。因此，我们提供DIKI作为一个免费的工具，也将适用于拖放数据分析的web界面(diki.limitedminds.org)。
{"title":"Developing an Incivility Dictionary for German Online Discussions – a Semi-Automated Approach Combining Human and Artificial Knowledge","authors":"Anke Stoll, L. Wilms, Marc Ziegele","doi":"10.1080/19312458.2023.2166028","DOIUrl":"https://doi.org/10.1080/19312458.2023.2166028","url":null,"abstract":"ABSTRACT Incivility in online discussions has become an important issue in political communication research. Instruments and tools for the automated analysis of uncivil content, however, are rare, especially for non-English user-generated text. In this study, we present a) an extensive dictionary (DIKI - Diktionär für Inzivilität, English: Dictionary for Incivility) to detect incivility in German-language online discussions, and b) a semi-automated two-step-approach that combines manual content analysis with automated keyword collection using a pre-trained word embedding model. We show that DIKI clearly outperforms comparable dictionaries that have been used as alternative instruments to measure incivility (e.g., the LIWC) as well as basic machine learning approaches to text classification. Further, we provide evidence that pre-trained word embeddings can fruitfully be employed in the explorative phase of creating dictionaries. Still, the manual evaluation of DIKI confirms that detecting complex and context-dependent forms of incivility remains challenging and constant update would be needed to maintain performance. Finally, the detailed documentation of the developing and evaluation process of DIKI may serve as a guideline for further research. We therefore provide DIKI as a freely available instrument that also will be applicable in a web interface for drag-and-drop data analysis (diki.limitedminds.org).","PeriodicalId":47552,"journal":{"name":"Communication Methods and Measures","volume":null,"pages":null},"PeriodicalIF":11.4,"publicationDate":"2023-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43968197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"文学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-19DOI: 10.1080/19312458.2023.2167965
Yingying Chen, Zhao Peng, Sei-Hill Kim, Chang Won Choi
ABSTRACT Topic modeling has become an effective tool for communication scholars to explore large amounts of texts. However, empirical studies applying topic modeling often face the critical question of making meaningful theoretical contributions. In this study, we highlighted the importance of theoretical underpinning, the research design, and the methodological details of topic modeling studies. We summarized five normative arguments that address critical issues in theory building and testing, research design, and reliability and validity assessments. Using these normative arguments as criteria, we systematically reviewed 105 communication studies that applied topic modeling. We identified gaps and missed opportunities in previous studies and discussed potential pitfalls for the field.
{"title":"What We Can Do and Cannot Do with Topic Modeling: A Systematic Review","authors":"Yingying Chen, Zhao Peng, Sei-Hill Kim, Chang Won Choi","doi":"10.1080/19312458.2023.2167965","DOIUrl":"https://doi.org/10.1080/19312458.2023.2167965","url":null,"abstract":"ABSTRACT Topic modeling has become an effective tool for communication scholars to explore large amounts of texts. However, empirical studies applying topic modeling often face the critical question of making meaningful theoretical contributions. In this study, we highlighted the importance of theoretical underpinning, the research design, and the methodological details of topic modeling studies. We summarized five normative arguments that address critical issues in theory building and testing, research design, and reliability and validity assessments. Using these normative arguments as criteria, we systematically reviewed 105 communication studies that applied topic modeling. We identified gaps and missed opportunities in previous studies and discussed potential pitfalls for the field.","PeriodicalId":47552,"journal":{"name":"Communication Methods and Measures","volume":null,"pages":null},"PeriodicalIF":11.4,"publicationDate":"2023-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47252637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"文学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-12DOI: 10.1080/19312458.2022.2156489
Brahim Zarouali, Theo Araujo, Jakob Ohme, Claes H. de Vreese
{"title":"Comparing Chatbots and Online Surveys for (Longitudinal) Data Collection: An Investigation of Response Characteristics, Data Quality, and User Evaluation","authors":"Brahim Zarouali, Theo Araujo, Jakob Ohme, Claes H. de Vreese","doi":"10.1080/19312458.2022.2156489","DOIUrl":"https://doi.org/10.1080/19312458.2022.2156489","url":null,"abstract":"","PeriodicalId":47552,"journal":{"name":"Communication Methods and Measures","volume":null,"pages":null},"PeriodicalIF":11.4,"publicationDate":"2023-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45616538","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"文学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01Epub Date: 2023-01-18DOI: 10.1080/19312458.2023.2167197
Miriam Brinberg, David M Lydon-Staley
Communication research often focuses on processes of communication, such as how messages impact individuals over time or how interpersonal relationships develop and change. Despite their importance, these change processes are often implicit in much theoretical and empirical work in communication. Intensive longitudinal data are becoming increasingly feasible to collect and, when coupled with appropriate analytic frameworks, enable researchers to better explore and articulate the types of change underlying communication processes. To facilitate the study of change processes, we (a) describe advances in data collection and analytic methods that allow researchers to articulate complex change processes of phenomena in communication research, (b) provide an overview of change processes and how they may be captured with intensive longitudinal methods, and (c) discuss considerations of capturing change when designing and implementing studies. We are excited about the future of studying processes of change in communication research, and we look forward to the iterations between empirical tests and theory revision that will occur as researchers delve into studying change within communication processes.
{"title":"Conceptualizing and Examining Change in Communication Research.","authors":"Miriam Brinberg, David M Lydon-Staley","doi":"10.1080/19312458.2023.2167197","DOIUrl":"10.1080/19312458.2023.2167197","url":null,"abstract":"<p><p>Communication research often focuses on <i>processes</i> of communication, such as how messages impact individuals over time or how interpersonal relationships develop and change. Despite their importance, these change processes are often implicit in much theoretical and empirical work in communication. Intensive longitudinal data are becoming increasingly feasible to collect and, when coupled with appropriate analytic frameworks, enable researchers to better explore and articulate the types of change underlying communication processes. To facilitate the study of change processes, we (a) describe advances in data collection and analytic methods that allow researchers to articulate complex change processes of phenomena in communication research, (b) provide an overview of change processes and how they may be captured with intensive longitudinal methods, and (c) discuss considerations of capturing change when designing and implementing studies. We are excited about the future of studying processes of change in communication research, and we look forward to the iterations between empirical tests and theory revision that will occur as researchers delve into studying change within communication processes.</p>","PeriodicalId":47552,"journal":{"name":"Communication Methods and Measures","volume":null,"pages":null},"PeriodicalIF":11.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10139745/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9404666","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"文学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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