Pub Date : 2023-12-07DOI: 10.1080/19312458.2023.2277958
Maria Schuld, Kevin Durrheim, Martin Mafunda
We propose a new method that embeds speakers into a spatial representation according to the linguistic similarity of their contributions to a debate. Such “speaker landscapes” can be constructed qu...
{"title":"Speaker landscapes: machine learning opens a window on the everyday language of opinion","authors":"Maria Schuld, Kevin Durrheim, Martin Mafunda","doi":"10.1080/19312458.2023.2277958","DOIUrl":"https://doi.org/10.1080/19312458.2023.2277958","url":null,"abstract":"We propose a new method that embeds speakers into a spatial representation according to the linguistic similarity of their contributions to a debate. Such “speaker landscapes” can be constructed qu...","PeriodicalId":47552,"journal":{"name":"Communication Methods and Measures","volume":null,"pages":null},"PeriodicalIF":11.4,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138559416","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 : 2023-11-27DOI: 10.1080/19312458.2023.2285766
J. Loes Pouwels, Theo Araujo, Wouter van Atteveldt, Marko Bachl, Patti M. Valkenburg
A pressing societal and scientific question is how social media use affects our cognitions, emotions, and behaviors. To answer this question, fine-grained insight into the content of individuals’ s...
{"title":"Integrating Communication Science and Computational Methods to Study Content-Based Social Media Effects","authors":"J. Loes Pouwels, Theo Araujo, Wouter van Atteveldt, Marko Bachl, Patti M. Valkenburg","doi":"10.1080/19312458.2023.2285766","DOIUrl":"https://doi.org/10.1080/19312458.2023.2285766","url":null,"abstract":"A pressing societal and scientific question is how social media use affects our cognitions, emotions, and behaviors. To answer this question, fine-grained insight into the content of individuals’ s...","PeriodicalId":47552,"journal":{"name":"Communication Methods and Measures","volume":null,"pages":null},"PeriodicalIF":11.4,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138455127","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 : 2023-11-27DOI: 10.1080/19312458.2023.2285765
Lukas Birkenmaier, Clemens M. Lechner, Claudia Wagner
Communication research frequently applies computational text analysis methods (CTAM) to detect and measure social science constructs. However, the validity of these measures can be difficult to ass...
{"title":"The Search for Solid Ground in Text as Data: A Systematic Review of Validation Practices and Practical Recommendations for Validation","authors":"Lukas Birkenmaier, Clemens M. Lechner, Claudia Wagner","doi":"10.1080/19312458.2023.2285765","DOIUrl":"https://doi.org/10.1080/19312458.2023.2285765","url":null,"abstract":"Communication research frequently applies computational text analysis methods (CTAM) to detect and measure social science constructs. However, the validity of these measures can be difficult to ass...","PeriodicalId":47552,"journal":{"name":"Communication Methods and Measures","volume":null,"pages":null},"PeriodicalIF":11.4,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138455094","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 : 2023-11-23DOI: 10.1080/19312458.2023.2277956
Yilang Peng, Irina Lock, Albert Ali Salah
To advance our understanding of social media effects, it is crucial to incorporate the increasingly prevalent visual media into our investigation. In this article, we discuss the theoretical opport...
{"title":"Automated Visual Analysis for the Study of Social Media Effects: Opportunities, Approaches, and Challenges","authors":"Yilang Peng, Irina Lock, Albert Ali Salah","doi":"10.1080/19312458.2023.2277956","DOIUrl":"https://doi.org/10.1080/19312458.2023.2277956","url":null,"abstract":"To advance our understanding of social media effects, it is crucial to incorporate the increasingly prevalent visual media into our investigation. In this article, we discuss the theoretical opport...","PeriodicalId":47552,"journal":{"name":"Communication Methods and Measures","volume":null,"pages":null},"PeriodicalIF":11.4,"publicationDate":"2023-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138297655","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 : 2023-11-15DOI: 10.1080/19312458.2023.2276542
Denise Solomon
Published in Communication Methods and Measures (Vol. 17, No. 4, 2023)
《通信方法与措施》(第17卷第4期，2023年)
{"title":"Acknowledgement","authors":"Denise Solomon","doi":"10.1080/19312458.2023.2276542","DOIUrl":"https://doi.org/10.1080/19312458.2023.2276542","url":null,"abstract":"Published in Communication Methods and Measures (Vol. 17, No. 4, 2023)","PeriodicalId":47552,"journal":{"name":"Communication Methods and Measures","volume":null,"pages":null},"PeriodicalIF":11.4,"publicationDate":"2023-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138293407","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 : 2023-11-14DOI: 10.1080/19312458.2023.2278177
Yini Zhang, Fan Chen, Jiyoun Suk, Zhiying Yue
ABSTRACTDespite the increasing use of digital media data in communication research, a central challenge persists – retrieving data with maximal accuracy and coverage. Our investigation of keyword-based data collection practices in extant communication research reveals a one-step process, whereas our cross-disciplinary literature review suggests an iterative query expansion process guided by human knowledge and computer intelligence. Hence, we introduce the WordPPR method for keyword selection and text data retrieval, which entails four steps: 1) collecting an initial dataset using core/seed keyword(s); 2) constructing a word graph based on the dataset; 3) applying the Personalized PageRank (PPR) algorithm to rank words in proximity to the seed keyword(s) and selecting new keywords that optimize retrieval precision and recall; 4) repeating steps 1–3 to determine if additional data collection is needed. Without requiring corpus-wide sampling/analysis or extensive manual annotation, this method is well suited for data collection from large-scale digital media corpora. Our simulation studies demonstrate its robustness against parameter choice and its improvement upon other methods in suggesting additional keywords. Its application in Twitter data retrieval is also provided. By advancing a more systematic approach to text data retrieval, this study contributes to improving digital media data retrieval practices in communication research and beyond. AcknowledgementWe thank our reviewers, the editors, Dr. Karl Rohe, Dr. Nojin Kwak, and Dr. Dhavan Shah for their helpful feedback. We also thank Rui Wang, Dongdong Yang, and Xinxia Dong for assistance with the journal article coding.Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe method and application code files as well as the supplementary materials are available at https://osf.io/pcybz/.Supplementary materialSupplemental data for this article can be accessed online at https://doi.org/10.1080/19312458.2023.2278177.Additional informationNotes on contributorsYini ZhangYini Zhang (Ph.D., University of Wisconsin–Madison) is an assistant professor in the Department of Communication at the University at Buffalo, State University of New York. She studies social media, media ecosystem, and political communication, using computational methods.Fan ChenFan Chen (Ph.D., University of Wisconsin–Madison) is a Data Scientist at Google. He studies and develops statistical methods for social media, genomics, and advertisement data. The bulk of this work was completed while he was a Ph.D. student at the University of Wisconsin–Madison.Jiyoun SukJiyoun Suk (Ph.D., University of Wisconsin-Madison) is an assistant professor in the Department of Communication at the University of Connecticut. She studies the role of networked communication in shaping social trust, activism, and polarization, using computational methods.Zhiying YueZhiying Yue (Ph.D., University
{"title":"WordPPR: A Researcher-Driven Computational Keyword Selection Method for Text Data Retrieval from Digital Media","authors":"Yini Zhang, Fan Chen, Jiyoun Suk, Zhiying Yue","doi":"10.1080/19312458.2023.2278177","DOIUrl":"https://doi.org/10.1080/19312458.2023.2278177","url":null,"abstract":"ABSTRACTDespite the increasing use of digital media data in communication research, a central challenge persists – retrieving data with maximal accuracy and coverage. Our investigation of keyword-based data collection practices in extant communication research reveals a one-step process, whereas our cross-disciplinary literature review suggests an iterative query expansion process guided by human knowledge and computer intelligence. Hence, we introduce the WordPPR method for keyword selection and text data retrieval, which entails four steps: 1) collecting an initial dataset using core/seed keyword(s); 2) constructing a word graph based on the dataset; 3) applying the Personalized PageRank (PPR) algorithm to rank words in proximity to the seed keyword(s) and selecting new keywords that optimize retrieval precision and recall; 4) repeating steps 1–3 to determine if additional data collection is needed. Without requiring corpus-wide sampling/analysis or extensive manual annotation, this method is well suited for data collection from large-scale digital media corpora. Our simulation studies demonstrate its robustness against parameter choice and its improvement upon other methods in suggesting additional keywords. Its application in Twitter data retrieval is also provided. By advancing a more systematic approach to text data retrieval, this study contributes to improving digital media data retrieval practices in communication research and beyond. AcknowledgementWe thank our reviewers, the editors, Dr. Karl Rohe, Dr. Nojin Kwak, and Dr. Dhavan Shah for their helpful feedback. We also thank Rui Wang, Dongdong Yang, and Xinxia Dong for assistance with the journal article coding.Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe method and application code files as well as the supplementary materials are available at https://osf.io/pcybz/.Supplementary materialSupplemental data for this article can be accessed online at https://doi.org/10.1080/19312458.2023.2278177.Additional informationNotes on contributorsYini ZhangYini Zhang (Ph.D., University of Wisconsin–Madison) is an assistant professor in the Department of Communication at the University at Buffalo, State University of New York. She studies social media, media ecosystem, and political communication, using computational methods.Fan ChenFan Chen (Ph.D., University of Wisconsin–Madison) is a Data Scientist at Google. He studies and develops statistical methods for social media, genomics, and advertisement data. The bulk of this work was completed while he was a Ph.D. student at the University of Wisconsin–Madison.Jiyoun SukJiyoun Suk (Ph.D., University of Wisconsin-Madison) is an assistant professor in the Department of Communication at the University of Connecticut. She studies the role of networked communication in shaping social trust, activism, and polarization, using computational methods.Zhiying YueZhiying Yue (Ph.D., University ","PeriodicalId":47552,"journal":{"name":"Communication Methods and Measures","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134991447","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 : 2023-10-04DOI: 10.1080/19312458.2023.2261372
Anne Kroon, Kasper Welbers, Damian Trilling, Wouter van Atteveldt
The availability of individual-level digital trace data offers exciting new ways to study media uses and effects based on the actual content that people encountered. In this article, we argue that ...
{"title":"Advancing Automated Content Analysis for a New Era of Media Effects Research: The Key Role of Transfer Learning","authors":"Anne Kroon, Kasper Welbers, Damian Trilling, Wouter van Atteveldt","doi":"10.1080/19312458.2023.2261372","DOIUrl":"https://doi.org/10.1080/19312458.2023.2261372","url":null,"abstract":"The availability of individual-level digital trace data offers exciting new ways to study media uses and effects based on the actual content that people encountered. In this article, we argue that ...","PeriodicalId":47552,"journal":{"name":"Communication Methods and Measures","volume":null,"pages":null},"PeriodicalIF":11.4,"publicationDate":"2023-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50164614","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 : 2023-09-14DOI: 10.1080/19312458.2023.2257595
Lukas P. Otto, Felicia Loecherbach, Rens Vliegenthart
Linkage analysis, i.e. linking media exposure, content, and surveys, has been a powerful tool to assess media effects. However, the development of online communication and the advent of social media brings about many challenges for traditional linkage designs. In this paper, we explain the three steps of linkage designs for online communication effects and the usage of computational approaches to capture communication exposure and content. We then review recent designs and studies that use different forms of digital trace data to link digital communication exposure, content, and surveys: Tracking data, data donations, and screenshots/screen recordings. We describe (practical) challenges and opportunities when linking digital communication traces with self-reports and show how these data could be analyzed to establish different media effects.
{"title":"Linkage Analysis Revised – Linking Digital Traces and Survey Data","authors":"Lukas P. Otto, Felicia Loecherbach, Rens Vliegenthart","doi":"10.1080/19312458.2023.2257595","DOIUrl":"https://doi.org/10.1080/19312458.2023.2257595","url":null,"abstract":"Linkage analysis, i.e. linking media exposure, content, and surveys, has been a powerful tool to assess media effects. However, the development of online communication and the advent of social media brings about many challenges for traditional linkage designs. In this paper, we explain the three steps of linkage designs for online communication effects and the usage of computational approaches to capture communication exposure and content. We then review recent designs and studies that use different forms of digital trace data to link digital communication exposure, content, and surveys: Tracking data, data donations, and screenshots/screen recordings. We describe (practical) challenges and opportunities when linking digital communication traces with self-reports and show how these data could be analyzed to establish different media effects.","PeriodicalId":47552,"journal":{"name":"Communication Methods and Measures","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134911720","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 : 2023-08-20DOI: 10.1080/19312458.2023.2246014
Regine Frener, Jana Dombrowski, Sabine Trepte
{"title":"Development and Validation of the Need for Privacy Scale (NFP-S)","authors":"Regine Frener, Jana Dombrowski, Sabine Trepte","doi":"10.1080/19312458.2023.2246014","DOIUrl":"https://doi.org/10.1080/19312458.2023.2246014","url":null,"abstract":"","PeriodicalId":47552,"journal":{"name":"Communication Methods and Measures","volume":null,"pages":null},"PeriodicalIF":11.4,"publicationDate":"2023-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44895190","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 : 2023-08-08DOI: 10.1080/19312458.2023.2242774
Yuvamathi Gandhi, A. Randall, Gabriel A. León, Hannah Martinson, L. Hocker, J. Bekki, B. Bernstein, Kerrie G. Wilkins-Yel
ABSTRACT Women of Color (WoC) in science, technology, engineering, and math (STEM) leave doctoral programs at disproportionately high rates. Supportive mentorship is key to increasing belonging and rates of retention, yet little is known about how conversations between mentees and their mentors on academic and personal stress topics unfold in real-time. Applying the lens of Social Cognitive Career Theory to communication dynamics between mentees and mentors, the present study utilized a dynamic dyadic systems (DDS) perspective to examine observationally coded data from six mentee-mentor dyads. First, hierarchical clustering analysis was applied to identify speaking turn types. Then, sequence analysis was used to identify common multi-turn patterns or conversation motifs (CM). Results showed five predominant CMs: (CM1) support provision through listening; (CM2) focus on mentor’s experience; (CM3) support provision through advice; (CM4) mentee’s making a bid for support; and (CM5) mentor dominated conversations. This study demonstrates methods for identifying potentially meaningful patterns of support in stress conversations between mentees and mentors. The application of such methods with larger samples may aid in understanding ways to increase retention among WoC in STEM through mentor support provision.
{"title":"A Dynamic Dyadic Systems Perspective on Communication of Real-Time Support Between Graduate Women in STEM and Their Mentor","authors":"Yuvamathi Gandhi, A. Randall, Gabriel A. León, Hannah Martinson, L. Hocker, J. Bekki, B. Bernstein, Kerrie G. Wilkins-Yel","doi":"10.1080/19312458.2023.2242774","DOIUrl":"https://doi.org/10.1080/19312458.2023.2242774","url":null,"abstract":"ABSTRACT Women of Color (WoC) in science, technology, engineering, and math (STEM) leave doctoral programs at disproportionately high rates. Supportive mentorship is key to increasing belonging and rates of retention, yet little is known about how conversations between mentees and their mentors on academic and personal stress topics unfold in real-time. Applying the lens of Social Cognitive Career Theory to communication dynamics between mentees and mentors, the present study utilized a dynamic dyadic systems (DDS) perspective to examine observationally coded data from six mentee-mentor dyads. First, hierarchical clustering analysis was applied to identify speaking turn types. Then, sequence analysis was used to identify common multi-turn patterns or conversation motifs (CM). Results showed five predominant CMs: (CM1) support provision through listening; (CM2) focus on mentor’s experience; (CM3) support provision through advice; (CM4) mentee’s making a bid for support; and (CM5) mentor dominated conversations. This study demonstrates methods for identifying potentially meaningful patterns of support in stress conversations between mentees and mentors. The application of such methods with larger samples may aid in understanding ways to increase retention among WoC in STEM through mentor support provision.","PeriodicalId":47552,"journal":{"name":"Communication Methods and Measures","volume":null,"pages":null},"PeriodicalIF":11.4,"publicationDate":"2023-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44331031","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}
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