Automated Content Analysis Research Articles

A longitudinal examination of anti-corruption disclosure in the banking sector of a least-developed economy: does board composition make a difference?

PurposeThis study focuses on anti-corruption disclosure (ACD) as a critical indicator of a bank’s commitment to combat corruption. It seeks to measure the level of ACD in banking companies. Emphasizing the pivotal role of board directors in ensuring corporate accountability and transparency, the study further explores the connection between board composition and ACD.Design/methodology/approachThis study adopts automated content analysis (using keyword search) to measure the level of ACD in the annual reports of listed banks in Bangladesh. Drawing upon agency theory and resource-dependence theory, the study proposes that the collective monitoring and resources facilitated by a well-structured board (size, gender diversity and independence) significantly influence a bank’s commitment to combat corruption. The paper employs linear regression to examine the hypotheses. The reliability of the findings is further validated through the application of the Generalized Method of Moments (GMM) and Quantile Regression.FindingsThe results indicate a steady rise in ACD over the sample period (2014–2022). The analysis establishes that larger board sizes and a greater presence of female directors are positively associated with ACD. Notably, the study identifies a critical mass of at least three female directors for a significant positive relationship between gender diversity on the board and ACD. However, no significant relationship is observed between board independence and ACD.Practical implicationsThe study sheds light on the current state of ACD within the banking companies of a least-developed country. The findings carry significant implications for regulators to gauge banks' dedication to anti-corruption efforts and make informed decisions on issuing guidance for enhanced ACD. These findings can also assist regulators in assessing the implications of board composition and formulating guidelines within the corporate governance code.Originality/valueThis study contributes to the literature on ACD in the context of a least-developed economy. Notably, the study fills a research gap by exploring ACD in the banking industry. A key aspect of this study is its exploration of the determinants of ACD, explicitly emphasizing how board composition is likely to influence ACD.

Can Generative AI improve social science?

Generative AI that can produce realistic text, images, and other human-like outputs is currently transforming many different industries. Yet it is not yet known how such tools might influence social science research. I argue Generative AI has the potential to improve survey research, online experiments, automated content analyses, agent-based models, and other techniques commonly used to study human behavior. In the second section of this article, I discuss the many limitations of Generative AI. I examine how bias in the data used to train these tools can negatively impact social science research—as well as a range of other challenges related to ethics, replication, environmental impact, and the proliferation of low-quality research. I conclude by arguing that social scientists can address many of these limitations by creating open-source infrastructure for research on human behavior. Such infrastructure is not only necessary to ensure broad access to high-quality research tools, I argue, but also because the progress of AI will require deeper understanding of the social forces that guide human behavior.

Bias in candidate sourcing communication: Investigating stereotypical gender- and age-related frames in online job advertisements at the sectoral level

Job advertisements hold a wealth of knowledge for the public relations field about complex internal and external organizational dynamics. They reflect enduring social and sectoral cultural proclivities and associated stereotypes about workers. We examine the presence of stereotypical frames in job advertisements from sectors with varying gender and age social group composition. Guided by social categorization framing and the stereotype content model, we operationalize stereotypical warmth- and competence-related frames in candidate sourcing communication. Automated content analysis was conducted on a dataset of online job ad sentences (n = 308,583) from 16,135 job ads. Results indicate warmth-related frames are most observed in ads from female-dominated (vs. male-dominated) sectors and younger-dominated (vs. older-dominated and mixed-age) sectors. Conversely, competence-related frames are most observed in ads from male-dominated (vs. female-dominated and mixed-age) sectors and older-dominated (vs. younger-dominated and mixed-age) sectors. We additionally find candidate gender stereotypes may supersede age stereotypes in hiring contexts. Implications are discussed in light of socialization and structuralist forces and their influence on organizational communication in homogeneous and heterogeneous sectors.

Making sense of data using automated content analysis: an illustration using archival data from newspaper articles

Making sense of data using automated content analysis: an illustration using archival data from newspaper articles

‘Wolf Warriors’ in the UN Security Council? Investigating power shifts through blaming

AbstractWhat shifts in the extent and patterns of China's blaming are observable at the United Nations Security Council (UNSC)? This article employs automated content analysis to measure the frequency of blaming by China and other permanent UNSC member states based on a dataset of all speeches (19,623 overall) in the UNSC from January 1995 to May 2022. Furthermore, it uses qualitative text analysis and network analysis to examine the targets of blaming in three different case studies of UN security governance—UN peacekeeping in former Yugoslavia (1998–2008), the UN sanctions regime against North Korea (2007–2017) and the broader peace and security discourse at the UNSC (2008–2018). The study conceptualizes Chinese blaming as a previously unexamined discursive strategy of compulsory power with potential long‐term effects on shifts in productive power. However, the results indicate the absence of blaming‐related shifts in the discursive space of the UNSC: China's rhetoric in the UNSC, compared with other UNSC members, has remained restrained over time. Variance in blaming behaviour between actors and across bilateral and multilateral settings seems to depend on strategic considerations that assess how to exert power most effectively.

Toward a Level Playing Field: An Analysis of the Language and Transparency of IT Risk

ABSTRACT We employ both manual and automated content analysis to update an existing dictionary (Boritz, Hayes, and Lim 2013) used to classify types of information technology weakness (ITW). Through analysis of auditors’ reports on internal control filed under Section 404 of the Sarbanes-Oxley Act from 2011 through 2020, we provide a robust tool for textual analytics. Additionally, we apply OpenAI’s large-language model neural networks for classification as a comparison point. We expand identification of ITW categories such as Design, Security, and Outsourcing and identify Governance as a critical new category in auditor reporting. We further expand the dictionary, capturing categories of IT risk found within current IT control frameworks (e.g., COBIT and AICPA TSCs), which we compare with content of auditor reporting. Evidence suggests stakeholders are subject to significant information asymmetry in assessing and reporting IT risk. We provide direction for future IT governance and improve application of advanced textual analysis.

The Seed of Doubt: Examining the Role of Alternative Social and News Media for the Birth of a Conspiracy Theory

Consuming conspiracy theories erodes trust in democratic institutions, while conspiracy beliefs demotivate democratic participation, posing a potential threat to democracy. The proliferation of social media, especially the emergence of numerous alternative platforms with minimal moderation, has greatly facilitated the dissemination and consumption of conspiracy theories. Nevertheless, there remains a dearth of knowledge concerning the origin and evolution of specific conspiracy theories across different platforms. This study aims to address this gap through a large-scale, cross-platform examination of the genesis of new conspiracy theories surrounding the death of Jeffrey Epstein. Through a (semi-) automated content analysis conducted on a distinctive dataset comprising N = 8,020,314 Epstein-related posts posted on both established platforms ( Twitter, Reddit) and alternative platforms ( Gab and 4Chan), we demonstrate that conspiracy theories emerge early and influence public discourse well in advance of reports from established media sources. Our data shows that users of the studied platforms immediately turn to conspirational explanations, exhibiting skepticism towards the official representation of events. Especially on alternative platforms, this skepticism swiftly transformed into unwarranted conspiracy theorizing, partly bolstered by references to alternative news media sources. The present study shows how conspirational explanations thrive in low information environments and how alternative media plays a role in turning rational skepticism into unwarranted conspiracy theories.

Automated content analysis as a tool to compare content in sexual selection research with examples of sexual selection in evolutionary biology textbooks: implications for teaching the nature of science

Background We used college-level evolution textbooks to examine the presentation of sexual selection research—a field with ongoing debates related to sex, sexuality and gender identity. Many classic sexual selection concepts have been criticized for androcentrism and other forms of gender-sex bias, specifically for de-emphasizing the female role in reproductive behaviors and over-reliance on gender-sex binaries. These classic concepts are fundamentally captured in the idea that animal reproductive-related behaviors can be grouped in sex roles (e.g. competitive males and selective females). Recently developed alternative concepts provide a more nuanced understanding of the flexibility of sexual and reproductive-related behaviors, stemming in part from growing attention to a broader range of female behavior. To assess whether students are receiving content reflecting these insights, we measured the congruence between textbook content and the scientific literature, using insects as a case study because of the importance of this group in the development of sexual selection theory, its prevalence in current sexual selection research, and the number of insect examples included in textbooks. We first coded textbook content for sexual selection concepts. We used automated content analysis to analyze a database of citations, keywords and abstracts in sexual selection research published between 1990 and 2014, inclusive of the period covered by the textbooks.ResultsThe textbooks and research literatures prioritized the same taxa (e.g., fruit flies) and sex roles as embodied in classic sexual selection theory. Both the research literature and some textbooks acknowledge androcentrism and other forms of gender-sex bias in classic sexual selection paradigms, especially competitive male and selective female sex roles. Yet, while the research literature included alternative models, textbooks neglected these alternatives, even when researchers had studied both classic and alternative views in the same insect.Conclusions We recommend using this kind of analysis of textbook content to engage students in a conversation around the social factors that impact knowledge construction, a key part of the epistemological understanding they need for a robust grasp of the Nature of Science and of evolutionary theory.

The moral foundations of responsible business: Using computational text analysis to explore the salience of morality in CSR communication

Morality is an integral part of Corporate Social Responsibility (CSR). However, the academic debate so far has mostly stated the importance of morality from a purely theoretical perspective, without empirically assessing it. We, therefore, draw on the moral foundations dictionary to conduct an automated content analysis of corporations’ (N = 277) annual CSR reports (N = 5010) over a period of 22 years. Looking into Germany, the United Kingdom, and the United States, we investigated morality salience, differences across sectors, countries, and over time as well as the association of morality with different stakeholder groups. Results inform the broader debate on CSR and morality and highlight avenues for future research.

Government-Nongovernmental Organization (NGO) Collaboration in Macao's COVID-19 Vaccine Promotion: Social Media Case Study.

The COVID-19 pandemic triggered unprecedented global vaccination efforts, with social media being a popular tool for vaccine promotion. This study probes into Macao's COVID-19 vaccine communication dynamics, with a focus on the multifaceted impacts of government agendas on social media. We scrutinized 22,986 vaccine-related Facebook posts from January 2020 to August 2022 in Macao. Using automated content analysis and advanced statistical methods, we unveiled intricate agenda dynamics between government and nongovernment entities. "Vaccine importance" and "COVID-19 risk" were the most prominent topics co-occurring in the overall vaccine communication. The government tended to emphasize "COVID-19 risk" and "vaccine effectiveness," while regular users prioritized vaccine safety and distribution, indicating a discrepancy in these agendas. Nonetheless, the government has limited impact on regular users in the aspects of vaccine importance, accessibility, affordability, and trust in experts. The agendas of government and nongovernment users intertwined, illustrating complex interactions. This study reveals the influence of government agendas on public discourse, impacting environmental awareness, public health education, and the social dynamics of inclusive communication during health crises. Inclusive strategies, accommodating public concerns, and involving diverse stakeholders are paramount for effective social media communication during health crises.

Research on fully automatic analysis methods for hydrogen energy

An ion chromatograph equipped with an anion exchange column was used to study the analytical methods for total halides and formic acid in hydrogen fuel. An automatic sampling method was established and compared with the manual injection method. A good linear relationship was observed between the peak area and concentration (r2 = 0.999), with a relative standard deviation (RSD) of less than 3% and a detection limit of the instrument at 0.01 mg/L. The experimental results indicate that this method has excellent repeatability and high sensitivity, enabling fully automatic analysis of total halides and formic acid content in hydrogen energy. This provides a fully automatic online analysis solution for the quality of hydrogen energy in the upstream, midstream, and downstream sectors.

Truths and Tales: Understanding Online Fake News Networks in South Korea

This study investigates the features of fake news networks and how they spread during the 2020 South Korean election. Using actor–network theory (ANT), we assessed the network’s central players and how they are connected. Results reveal the characteristics of the videoclips and channel networks responsible for the propagation of fake news. Analysis of the videoclip network reveals a high number of detected fake news videos and a high density of connections among users. Assessment of news videoclips on both actual and fake news networks reveals that the real news network is more concentrated. However, the scale of the network may play a role in these variations. Statistics for network centralization reveal that users are spread out over the network, pointing to its decentralized character. A closer look at the real and fake news networks inside videos and channels reveals similar trends. We find that the density of the real news videoclip network is higher than that of the fake news network, whereas the fake news channel networks are denser than their real news counterparts, which may indicate greater activity and interconnectedness in their transmission. We also found that fake news videoclips had more likes than real news videoclips, whereas real news videoclips had more dislikes than fake news videoclips. These findings strongly suggest that fake news videoclips are more accepted when people watch them on YouTube. In addition, we used semantic networks and automated content analysis to uncover common language patterns in fake news, which helps us better understand the structure and dynamics of the networks involved in the dissemination of fake news. The findings reported here provide important insights on how fake news spread via social networks during the South Korean election of 2020. The results of this study have important implications for the campaign against fake news and ensuring factual coverage.

Anti-Immigrant Rhetoric and ICE Reporting Interest: Evidence from a Large-Scale Study of Web Search Data

Abstract This paper studies whether media cues can motivate interest in reporting suspected unauthorized immigrants to Immigration and Customs Enforcement (ICE). Using web search data and automated content analysis of cable news transcripts, we examine the role of media coverage on searches for how to report immigrants to ICE and searches about immigrant crime and welfare dependency. We find significant and persistent increases in news segments on crime by after Trump's inauguration, accompanied by a sharp increase in searches for how to report immigrants. We find a strong association between daily reporting searches and immigration and crime coverage. Using searches during broadcasts of presidential speeches, we isolate the specific effect of anti-immigrant media coverage on searches for how to report immigrants to ICE. The findings indicate that the media's choices regarding the coverage of immigrants can have a strong impact on the public's interest in behaviour that directly harms immigrants.

Misclassification in Automated Content Analysis Causes Bias in Regression. Can We Fix It? Yes We Can!

ABSTRACT Automated classifiers (ACs), often built via supervised machine learning (SML), can categorize large, statistically powerful samples of data ranging from text to images and video. They have become widely popular measurement devices in communication science and related fields. Despite this popularity, even highly accurate classifiers make errors that cause misclassification bias and misleading results when input to downstream statistical analyses–unless such analyses account for these errors. As we show in a systematic literature review of SML applications, communication scholars largely ignore misclassification bias. In principle, existing statistical methods can use “gold standard” validation data, such as that created by human annotators, to correct misclassification bias. We introduce and test such methods, including a new method we design and implement in the R package misclassification_models, via Monte Carlo simulations designed to reveal each method’s limitations, which we also release. Based on our results, we recommend our new error correction method as it is versatile and efficient. In sum, automated classifiers, even those below common accuracy standards or those making systematic misclassifications, can be useful for measurement with careful study design and appropriate error correction methods.

ESG themes of the restaurant industry: a comparison between full-service and limited-service restaurants

ABSTRACT Considering the increasing significance of environmental, social, and governance (ESG) in the corporate world, an exploration of ESG reporting in the hospitality context has received limited attention. Accordingly, based on the stakeholder and institutional theories, this study proposes to reveal the overall and contemporary status of the restaurant industry’s ESG practices and further compare the ESG engagements of full-service restaurants and limited-service restaurants. This study collects data from publicly traded restaurant companies’ ESG reports and employs automated content analysis based on a computer-assisted qualitative data analysis approach. The findings of this study provide value to the ESG literature and practical implications

The industry-academia gap on the corporate governance issues in tourism and hospitality: A critical review and future research suggestions

ABSTRACT The current study identifies the most important corporate governance topics being discussed in the industry and academia. The findings are based on a series of comparative automated content analyses of 742 public tourism and hospitality companies’ governance statements published between 2013 and 2022 and on 129 corporate governance-related journal articles in tourism and hospitality. The findings show that industry’s top corporate governance themes are executive compensation, board of directors’ qualifications, and voting. The paper compares these with academia’s top five themes: firm performance, corporate social responsibility, management practices, local development, and growth opportunities. Future research directions are also presented.

University- Creative Industry Linkages in Art and Design Higher Education: Insights From Indonesia and Taiwan

This article presents a study of the university-industry linkages in art and design higher education, focusing on the collaborative efforts between universities and the creative sector in Indonesia and Taiwan. Using a qualitative approach, in-depth interviews and literature reviews were conducted. An automated content analysis of seven semi-structured interviews with lecturers, designers, and the government stressed the importance of university and industry linkages. This study found that despite the prevalent practice of industrial cooperation with universities in the Indonesian higher education system, challenges persist in effectively connecting graduates with employment opportunities in the creative industry sectors. In response, the Indonesian government and universities have implemented policies to support collaboration programs to address these challenges. Conversely, Taiwan has recognized the potential of its creative industry assets to create a promising market and has placed significant value on the benefits derived from industry-university collaboration. Consequently, Taiwan has proactively developed its creative industries, receiving substantial encouragement and support from the local government.

Few-shot learning for automated content analysis: Efficient coding of arguments and claims in the debate on arms deliveries to Ukraine

Pre-trained language models (PLM) based on transformer neural networks developed in the field of natural language processing (NLP) offer great opportunities to improve automatic content analysis in communication science, especially for the coding of complex semantic categories in large datasets via supervised machine learning. However, three characteristics so far impeded the widespread adoption of the methods in the applying disciplines: the dominance of English language models in NLP research, the necessary computing resources, and the effort required to produce training data to fine-tune PLMs. In this study, we address these challenges by using a multilingual transformer model in combination with the adapter extension to transformers, and few-shot learning methods. We test our approach on a realistic use case from communication science to automatically detect claims and arguments together with their stance in the German news debate on arms deliveries to Ukraine. In three experiments, we evaluate (1) data preprocessing strategies and model variants for this task, (2) the performance of different few-shot learning methods, and (3) how well the best setup performs on varying training set sizes in terms of validity, reliability, replicability and reproducibility of the results. We find that our proposed combination of transformer adapters with pattern exploiting training provides a parameter-efficient and easily shareable alternative to fully fine-tuning PLMs. It performs on par in terms of validity, while overall, provides better properties for application in communication studies. The results also show that pre-fine-tuning for a task on a near-domain dataset leads to substantial improvement, in particular in the few-shot setting. Further, the results indicate that it is useful to bias the dataset away from the viewpoints of specific prominent individuals.

Circular Economy Implementation Status of Selected ASEAN Countries

Developed countries have adopted the circular economy concept to solve environmental problems. This study underpins the paradox theory to investigate the circular economy’s implementation status in the ASEAN region through the circular economy index. The circular economy index disclosure data for top companies in the ASEAN region was collected through an automated content analysis approach using DICTION on the annual/sustainability reports. The findings show that most public companies from the ASEAN region disclose the circular economy indicators in their annual/sustainability reports. The environment-sensitive firms are disclosing the circular economy indicators actively compared to the non-environment sensitive companies. Based on the results, this study provides theoretical and practical implications. This study is also limited because it is based only on the ASEAN region. Future studies can underpin other theoretical support and provide a comparative analysis across the continents.

Percepção de bolsistas Capes sobre o impacto no trabalho científico da formação pós-doutoral no exterior

Abstract The objective of this research is to analyze the impact of Capes’ postdoctoral training policy abroad (pd_training) on the insertion or quality of the scholarship holder’s work in their department/group in Brazil. The article empirically discusses post-doctorate as a strategy for the internationalization of science and adopts a qualitative approach, based on the automated content analysis technique, to investigate the data collected at the end of the scholarship holders’ activities by Capes itself. The results point to positive impacts, connected to the common perception of increased possibility of collaboration between national and foreign groups, as well as international visibility arising from relevant scientific publications. The positive impacts are also connected to specific perceptions, directly associated with the types of scholarships received and indirectly with the profile of the scholarship holder. Specifically, Visiting Professor scholarship holders tend to cite, as an impact of their training, the possibility of greater international insertion of the departments to which they are linked and greater mobility of students and researchers who are part of their research group in Brazil. Those who have a Post-Doctoral scholarship highlight greater professional insertion, related to opportunities in the job market for scientists in Brazil or the lack thereof. Those who have a Post-Doctoral Internship scholarship tend to associate the acquisition and application of new knowledge and techniques as a positive effect of their training. In conclusion, the results point to the importance of the articulation between the profile of the scholarship holders and the intentionality projected for the public training policy_pd, aiming to guarantee its effectiveness.