scholarly journals Clouded reality: News representations of culturally close and distant ethnic outgroups

2020 ◽  
Vol 45 (s1) ◽  
pp. 744-764 ◽  
Author(s):  
Anne C. Kroon ◽  
Damian Trilling ◽  
Toni G. L. A. van der Meer ◽  
Jeroen G. F. Jonkman
Keyword(s):  
Content Analysis ◽  
Cultural Distance ◽  
Word Embeddings ◽  
Ethnic Bias ◽  
Automated Content Analysis ◽  
Newspaper Articles

AbstractThe current study explores how the cultural distance of ethnic outgroups relative to the ethnic ingroup is related to stereotypical news representations. It does so by drawing on a sample of more than three million Dutch newspaper articles and uses advanced methods of automated content analysis, namely word embeddings. The results show that distant ethnic outgroup members (i. e., Moroccans) are associated with negative characteristics and issues, while this is not the case for close ethnic outgroup members (i. e., Belgians). The current study demonstrates the usefulness of word embeddings as a tool to study subtle aspects of ethnic bias in mass-mediated content.

scholarly journals Sentiment/tone (Automated Content Analysis)

10.34778/1d ◽  
2021 ◽  
Author(s):  
Valerie Hase
Keyword(s):  
Machine Learning ◽  
Content Analysis ◽  
Sentiment Analysis ◽  
Supervised Machine Learning ◽  
Data Sets ◽  
Financial News ◽  
State Of The Union ◽  
Automated Content Analysis ◽  
Course Material ◽  
Newspaper Articles

Sentiment/tone describes the way issues or specific actors are described in coverage. Many analyses differentiate between negative, neutral/balanced or positive sentiment/tone as broader categories, but analyses might also measure expressions of incivility, fear, or happiness, for example, as more granular types of sentiment/tone. Analyses can detect sentiment/tone in full texts (e.g., general sentiment in financial news) or concerning specific issues (e.g., specific sentiment towards the stock market in financial news or a specific actor). The datasets referred to in the table are described in the following paragraph: Puschmann (2019) uses four data sets to demonstrate how sentiment/tone may be analyzed by the computer. Using Sherlock Holmes stories (18th century, N = 12), tweets (2016, N = 18,826), Swiss newspaper articles (2007-2012, N = 21,280), and debate transcripts (2013-2017, N = 205,584), he illustrates how dictionaries may be applied for such a task. Rauh (2019) uses three data sets to validate his organic German language dictionary for sentiment/tone. His data consists of sentences from German parliament speeches (1991-2013, N = 1,500), German-language quasi-sentences from German, Austrian and Swiss party manifestos (1998-2013, N = 14,008) and newspaper, journal and news wire articles (2011-2012, N = 4,038). Silge and Robinson (2020) use six Jane Austen novels to demonstrate how dictionaries may be used for sentiment analysis. Van Atteveldt and Welbers (2020) use state of the Union speeches (1789-2017, N = 58) for the same purpose. The same authors (van Atteveldt & Welbers, 2019) show based on a dataset of N = 2,000 movie reviews how supervised machine learning might also do the trick. In their Quanteda tutorials, Watanabe and Müller (2019) demonstrate the use of dictionaries and supervised machine learning for sentiment analysis on UK newspaper articles (2012-2016, N = 6,000) as well as the same set of movie reviews (n = 2,000). Lastly, Wiedemann and Niekler (2017) use state of the Union speeches (1790-2017, N = 233) to demonstrate how sentiment/tone can be coded automatically via a dictionary approach. Field of application/theoretical foundation: Related to theories of “Framing” and “Bias” in coverage, many analyses are concerned with the way the news evaluates and interprets specific issues and actors. References/combination with other methods of data collection: Manual coding is needed for many automated analyses, including the ones concerned with sentiment. Studies for example use manual content analysis to develop dictionaries, to create training sets on which algorithms used for automated classification are trained, or to validate the results of automated analyses (Song et al., 2020).   Table 1. Measurement of “Sentiment/Tone” using automated content analysis. Author(s) Sample Procedure Formal validity check with manual coding as benchmark* Code Puschmann (2019) (a) Sherlock Holmes stories (b) Tweets (c) Swiss newspaper articles (d) German Parliament transcripts   Dictionary approach Not reported http://inhaltsanalyse-mit-r.de/sentiment.html Rauh (2018) (a) Bundestag speeches (b) Quasi-sentences from German, Austrian and Swiss party manifestos (c) Newspapers, journals, agency reports Dictionary approach Reported https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/BKBXWD Silge & Robinson (2020) Books by Jane Austen Dictionary approach Not reported https://www.tidytextmining.com/sentiment.html van Atteveldt & Welbers (2020) State of the Union speeches Dictionary approach Reported https://github.com/ccs-amsterdam/r-course-material/blob/master/tutorials/sentiment_analysis.md van Atteveldt & Welbers (2019) Movie reviews Supervised Machine Learning Approach Reported https://github.com/ccs-amsterdam/r-course-material/blob/master/tutorials/r_text_ml.md Watanabe & Müller (2019) Newspaper articles Dictionary approach Not reported https://tutorials.quanteda.io/advanced-operations/targeted-dictionary-analysis/ Watanabe & Müller (2019) Movie reviews Supervised Machine Learning Approach Reported https://tutorials.quanteda.io/machine-learning/nb/ Wiedemann & Niekler (2017) State of the Union speeches Dictionary approach Not reported https://tm4ss.github.io/docs/Tutorial_3_Frequency.html *Please note that many of the sources listed here are tutorials on how to conducted automated analyses – and therefore not focused on the validation of results. Readers should simply read this column as an indication in terms of which sources they can refer to if they are interested in the validation of results. References Puschmann, C. (2019). Automatisierte Inhaltsanalyse mit R. Retrieved from http://inhaltsanalyse-mit-r.de/index.html Rauh, C. (2018). Validating a sentiment dictionary for German political language—A workbench note. Journal of Information Technology & Politics, 15(4), 319–343. doi:10.1080/19331681.2018.1485608 Silge, J., & Robinson, D. (2020). Text mining with R. A tidy approach. Retrieved from https://www.tidytextmining.com/ Song, H., Tolochko, P., Eberl, J.-M., Eisele, O., Greussing, E., Heidenreich, T., Lind, F., Galyga, S., & Boomgaarden, H.G. (2020) In validations we trust? The impact of imperfect human annotations as a gold standard on the quality of validation of automated content analysis. Political Communication, 37(4), 550-572. van Atteveldt, W., & Welbers, K. (2019). Supervised Text Classification. Retrieved from https://github.com/ccs-amsterdam/r-course-material/blob/master/tutorials/r_text_ml.md van Atteveldt, W., & Welbers, K. (2020). Supervised Sentiment Analysis in R. Retrieved from https://github.com/ccs-amsterdam/r-course-material/blob/master/tutorials/sentiment_analysis.md Watanabe, K., & Müller, S. (2019). Quanteda tutorials. Retrieved from https://tutorials.quanteda.io/ Wiedemann, G., Niekler, A. (2017). Hands-on: a five day text mining course for humanists and social scientists in R. Proceedings of the 1st Workshop Teaching NLP for Digital Humanities (Teach4DH@GSCL 2017), Berlin. Retrieved from https://tm4ss.github.io/docs/index.html

scholarly journals Actors (Automated Content Analysis)

10.34778/1b ◽  
2021 ◽  
Author(s):  
Valerie Hase
Keyword(s):  
Content Analysis ◽  
Named Entities ◽  
Part Of Speech Tagging ◽  
Part Of Speech ◽  
State Of The Union ◽  
Automated Content Analysis ◽  
German Newspaper ◽  
Speech Tagging ◽  
Validation Of Results ◽  
Newspaper Articles

Actors in coverage might be individuals, groups, or organizations, which are discussed, described, or quoted in the news. The datasets referred to in the table are described in the following paragraph: Benoit and Matuso (2020) uses fictional sentences (N = 5) to demonstrate how named entities and noun phrases can be identified automatically. Lind and Meltzer (2020) demonstrate the use of organic dictionaries to identify actors in German newspaper articles (2013-2017, N = 348,785). Puschmann (2019) uses four data sets to demonstrate how sentiment/tone may be analyzed by the computer. Using tweets (2016, N = 18,826), German newspaper articles (2011-2016, N = 377), Swiss newspaper articles (2007-2012, N = 21,280), and debate transcripts (1970-2017, N = 7,897), he extracts nouns and named entities from text. Lastly, Wiedemann and Niekler (2017) extract proper nouns from State of the Union speeches (1790-2017, N = 233). Field of application/theoretical foundation: Related to theories of “Agenda Setting” and “Framing”, analyses might want to know how much weight is given to a specific actor, how these actors are evaluated and what perspectives and frames they might bring into the discussion how prominently. References/combination with other methods of data collection: Oftentimes, studies use both manual and automated content analysis to identify actors in text. This might be a useful tool to extend the lists of actors that can be found as well as to validate automated analyses. For example, Lind and Meltzer (2020) combine manual coding and dictionaries to identify the salience of women in the news.   Table 1. Measurement of “Actors” using automated content analysis. Author(s) Sample Procedure Formal validity check with manual coding as benchmark* Code Benoit & Matuso (2020) Fictional sentences Part-of-Speech tagging; syntactic parsing Not reported https://cran.r-project.org/web/packages/spacyr/vignettes/using_spacyr.html Lind & Meltzer (2020) Newspapers Dictionary approach Reported https://osf.io/yqbcj/?view_only=369e2004172b43bb91a39b536970e50b Puschmann (2019) (a) Tweets (b) German newspaper articles (c) Swiss newspaper articles (d) United Nations General Debate Transcripts Part-of-Speech tagging; syntactic parsing Not reported http://inhaltsanalyse-mit-r.de/ner.html Wiedemann & Niekler (2017) State of the Union speeches Part-of-Speech tagging Not reported https://tm4ss.github.io/docs/Tutorial_8_NER_POS.html *Please note that many of the sources listed here are tutorials on how to conducted automated analyses – and therefore not focused on the validation of results. Readers should simply read this column as an indication in terms of which sources they can refer to if they are interested in the validation of results. References Benoit, K., & Matuso. (2020). A Guide to Using spacyr. Retrieved from https://cran.r-project.org/web/packages/spacyr/vignettes/using_spacyr.html Lind, F., & Meltzer, C. E. (2020). Now you see me, now you don’t: Applying automated content analysis to track migrant women’s salience in German news. Feminist Media Studies, 1–18. Puschmann, C. (2019). Automatisierte Inhaltsanalyse mit R. Retrieved from http://inhaltsanalyse-mit-r.de/index.html Wiedemann, G., Niekler, A. (2017). Hands-on: a five day text mining course for humanists and social scientists in R. Proceedings of the 1st Workshop Teaching NLP for Digital Humanities (Teach4DH@GSCL 2017), Berlin. Retrieved from https://tm4ss.github.io/docs/index.html

scholarly journals Frames (Automated Content Analysis)

10.34778/1c ◽  
2021 ◽  
Author(s):  
Valerie Hase
Keyword(s):  
Content Analysis ◽  
Network Analysis ◽  
Topic Modeling ◽  
Semantic Networks ◽  
Newspaper Coverage ◽  
Journalism Studies ◽  
Detection Algorithms ◽  
Automated Content Analysis ◽  
Validation Of Results ◽  
Newspaper Articles

Frames describe the way issues are presented, i.e., what aspects are made salient when communicating about these issues. Field of application/theoretical foundation: The concept of frames is directly based on the theory of “Framing”. However, many studies using automated content analysis are lacking a clear theoretical definition of what constitutes a frame. As an exception, Walter and Ophir (2019) use automated content analysis to explore issue and strategy frames as defined by Cappella and Jamieson (1997). Vu and Lynn (2020) refer to Entman’s (1991) understanding of frames. The datasets referred to in the table are described in the following paragraph: Van der Meer et al. (2010) use a dataset consisting of Dutch newspaper articles (1991-2015, N = 9,443) and LDA topic modeling in combination with k-means clustering to identify frames. Walter and Ophir (2019) use three different datasets and a combination of topic modeling, network analysis and community detection algorithms to analyze frames. Their datasets consist of political newspaper articles and wire service coverage (N = 8,337), newspaper articles on foreign nations (2010-2015, N = 18,216) and health-related newspaper coverage (2009-2016, N = 5,005). Lastly, Vu and Lynn (2020) analyze newspaper coverage of the Rohingya crisis (2017-2018, N = 747) concerning frames. References/combination with other methods of data collection: While most approaches only rely on automated data collection and analyses, some also combine automated and manual coding. For example, a recent study by Vu and Lynn (2020) proposes to combine semantic networks and manual coding to identify frames.   Table 1. Measurement of “Frames” using automated content analysis. Author(s) Sample Procedure Formal validity check with manual coding as benchmark* Code Vu & Lynn (2020) Newspaper articles Semantic networks; manual coding Reported Not available van der Meer et al. (2019) Newspaper articles LDA topic modeling; k-means clustering Not reported Not available Walter & Ophir (2019) (a) U.S. newspapers and wire service articles (b) Newspaper articles (c) Newspaper articles     LDA topic modeling, network analysis; community detection algorithms Not reported https://github.com/DrorWalt/ANTMN *Please note that many of the sources listed here are tutorials on how to conducted automated analyses – and therefore not focused on the validation of results. Readers should simply read this column as an indication in terms of which sources they can refer to if they are interested in the validation of results. References Cappella, J. N., & Jamieson, K. H. (1997). Spiral of cynicism: The press and the public good. New York: Oxford University Press. Entman, R. M. 1991. Framing U.S. coverage of international news: contrasts in narratives of the KAL and Iran Air incidents. Journal of Communication, 41(4), 6-7. van der Meer, T. G. L. A., Kroon, A. C., Verhoeven, P., & Jonkman, J. (2019). Mediatization and the disproportionate attention to negative news: The case of airplane crashes. Journalism Studies, 20(6), 783–803. Walter, D., & Ophir, Y. (2019). News frame analysis: an inductive mixed-method computational approach. Communication Methods and Measures, 13(4), 248–266. Vu, H. T., & Lynn, N. (2020). When the news takes sides: Automated framing analysis of news coverage of the rohingya crisis by the elite press from three countries. Journalism Studies. Online first publication. doi:10.1080/1461670X.2020.1745665

scholarly journals Medication-related harm and the newspapers - what has been communicated to the public in Ireland: A systematic content analysis

2021 ◽  
Vol 29 (Supplement_1) ◽  
pp. i28-i29
Author(s):  
R Munshi ◽  
T Grimes
Keyword(s):  
Patient Safety ◽  
Content Analysis ◽  
Quantitative Data ◽  
Data Extraction ◽  
Life Stage ◽  
Opioid Analgesics ◽  
Daily Mail ◽  
The Public ◽  
Related Harm ◽  
Newspaper Articles

Abstract Introduction Reducing the global prevalence of severe, avoidable medication-related harm (MRH) by 50% by the end of 2022 is the WHO’s third global patient safety challenge [1]. MRH is reported frequently in the academic literature, with increasing age being a key risk factor. The WHO have highlighted the need to improve public health literacy and knowledge about medications. Little is known about the frequency and nature of Irish newspaper reports about MRH. This study sought to address this gap and to examine reporting during the calendar years 2019 and 2009. Methods In this mixed-methods study, LexisNexis® [2], an online newspaper archive database, was searched for newspaper articles reporting on MRH, published in the Republic of Ireland during the calendar years 2019 and 2009. The search strategy focussed on “medication” AND “harm” AND “patient”. Quantitative data extraction aimed to describe the frequency (by count of articles) of reporting of MRH and the nature by describing the publishing newspaper titles and the reported details of: drug class(es), demographics (age or life stage, gender) of those experiencing harm and the severity of harm. Qualitatively, a systematic content analysis, using inductive coding is ongoing and will be reported separately. Research ethics committee approval for this study is not required because this is an analysis of material in the public domain. Results In total, 7098 newspaper articles were identified through database searching for 2019 (n=3217) and 2009 (n=3881). To date, 54% (3867: n=3217, 45% 2019, n=650, 9% 2009) of these were screened, of which 63 newspaper articles (n=44 2019, n=19 2009) were included and quantitative data were extracted. Within these 63 articles, 71 cases of individual people experiencing MRH were reported (52 in 2019 and 19 in 2009). The newspapers most commonly reporting MRH were Irish Daily Mail (31/63: 27 in 2019 and 4 in 2009) and Irish Times (17/63:9 in 2019 and 8 in 2009). Drug classes most frequently reported as causing MRH were central nervous system drugs (antiepileptics n=10, opioid analgesics n=5, antidepressants n=9, and anxiolytics n=1), cancer chemotherapy (23 cases) and non-steroidal anti-inflammatories (n=3). MRH was reported as being fatal (13 /71:8 in 2019 and 5 in 2009) and non-fatal (58/71), with seven cases (5 in 2019 and 2 in 2009) of permanent harm. Among the 71 individual cases of MRH, the majority were adults aged 18–64 years (n=36), children (n=7), older adults (n=8), foetus (n=3) and newborn (n=1), while the remainder did not report the person’s age. Conclusion MRH is frequently reported to the public through Irish newspapers. The study is limited by focus on newsprint media with the exclusion of other forms of digital or social media and restriction to two calendar years in a single country, which likely stifles the generalisability of findings to other contexts. Future work could explore this issue across a wider range of media platforms and examine changes in reporting over time. The study findings may support an agenda to improve the general public's exposure to information and knowledge of MRH and medication safety. References 1. Donaldson, L.J., et al., Medication without harm: WHO's third global patient safety challenge. 2017. 389(10080): p. 1680–1681. 2. https://advance-lexis-com.elib.tcd.ie/firsttime?crid=d5f713e8-8107-4efd-91cc-1e99c82cdb58&pdmfid=1519360.

scholarly journals Utility-Scale Solar in the Great Lakes: Analyzing Community Reactions to Solar Developments

2021 ◽  
Vol 13 (4) ◽  
pp. 1677
Author(s):  
Emma Uebelhor ◽  
Olivia Hintz ◽  
Sarah B. Mills ◽  
Abigail Randall
Keyword(s):  
Content Analysis ◽  
Great Lakes ◽  
Energy Production ◽  
Community Support ◽  
Community Perceptions ◽  
Global Impact ◽  
Local Newspaper ◽  
Great Lakes States ◽  
Utility Scale ◽  
Newspaper Articles

In the coming years, it is expected that reliance on utility-scale solar projects for energy production will increase exponentially. As a result, communities throughout the Midwest will become potential solar facility hosts. Previous research has sought to identify factors that influence community support and opposition to solar developments throughout the country. This paper builds upon prior research by examining community perceptions about the economic, environmental, local and global impact of solar projects in four Great Lakes states using a content analysis of local newspaper articles. Ultimately, this paper identifies the most common perceptions of solar facilities and offers some preliminary suggestions on strategies to mitigate the most prevalent concerns.

scholarly journals The War of the Words: How Linguistic Differences in Reporting Shape Perceptions of Terrorism

2016 ◽  
Author(s):  
Elizabeth Dunn ◽  
Moriah Moore ◽  
Brian A. Nosek
Keyword(s):  
Public Policy ◽  
Content Analysis ◽  
Word Usage ◽  
Linguistic Differences ◽  
News Reporting ◽  
Violent Acts ◽  
People’S Attitudes ◽  
The U.S ◽  
Newspaper Articles ◽  
Violent Events

In four studies, we demonstrate that subtle linguistic differences in news reporting are sufficient to influence whether people interpret violent acts as patriotism or terrorism. In Study 1, a content analysis of newspaper articles describing violence in Iraq revealed that words implying destruction and devious intent were typically used in reference to violent actions associated with Iraq and opponents of the U.S., while more benign words were used in reference to the U.S. and its allies. These observed differences in word usage establish schemas that guide perception of violence as terrorism or patriotism, thereby affecting people’s attitudes toward (Study 2) and memory for (Studies 3 and 4) violent events. Implications for the media’s impact on public policy are discussed.

The Power and The Truth. A Film. A National Project

2021 ◽  
Vol 66 (Special Issue) ◽  
pp. 171-194
Author(s):  
Ion Indolean ◽  
◽  
Keyword(s):  
Content Analysis ◽  
National Project ◽  
The Press ◽  
Political Film ◽  
The Moment ◽  
Nicolae Ceausescu ◽  
The Way ◽  
Newspaper Articles

"This article tries to understand what type of film is approved by the Nicolae Ceauşescu regime and how it is promoted, through various propaganda channels. In this sense, we choose to discuss the film made by the artistic couple Manole Marcus - Titus Popovici, The Power and The Truth (1972), and we resort to a content analysis to understand the way it was made. We are also interested in the echoes of the film in the press of the time and how with the help of newspaper articles the authorities inoculate the idea that this film is the most important cinematographic achievement of the moment, a benchmark for political productions to be made from that point on. Keywords: Cinematography, Political Film, Nicolae Ceauşescu, Manole Marcus, Titus Popovici, Propaganda "

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