Automated Content Analysis Research Articles

V4Design: Intelligent Analysis and Integration of Multimedia Content for Creative Industries

Nowadays, vast amounts of multimedia content are being produced, archived, and digitized, resulting in great troves of data of interest. Examples include user-generated content, such as images, videos, text, and audio posted by users on social media and wikis, or content provided through official publishers and distributors, such as digital libraries, organizations, and online museums. This digital content can serve as a valuable source of inspiration to the creative industries, such as architecture and gaming, to produce new innovative assets or to enhance and (re-)use existing ones. However, in its current form, this content is difficult to be reused and repurposed due to the lack of appropriate solutions for its retrieval, analysis, and integration into the design process. In this article, we present V4Design, a novel framework for the automatic content analysis, linking, and seamless transformation of heterogeneous multimedia content to help architects and virtual reality game designers establish innovative value chains and end-user applications. By integrating and intelligently combining state-of-the-art technologies in computer vision, 3-D generation, text analysis, generation and semantic integration, and interlinking, V4Design provides architects and video game designers with innovative tools to draw inspiration from archive footage and documentaries, inspiring and eventually supporting the design process.

Influences of Media Routines on Fact-Checking:

This exploratory study examined PolitiFact fact-checks (N=18,446) published between 2008 and 2020 to understand the extent to which the largest political fact-checking network in the United States utilizes traditional media routines in finding check-worthy claims and gathering information to verify claims. An automated content analysis revealed that PolitiFact relies more on routine channels of news production to find check-worthy claims than non-routine channels. The results also show that non-elite sources account for a negligible portion of PolitiFact sources, but the organization uses more non-traditional channels to find sources.

Ups and downs on “r/science” — exploring the dynamics of science communication on Reddit

This exploratory study analyzed user-generated science communication on Reddit from May 2007 to October 2018 ($n=694.147$ posts). We used automated content analyses and topic modelling to explore patterns that the user-generated communication exhibits. Results indicate that science communication on “r/science” refers to a broad range of different topics and disciplines. Specific upvote features of Reddit result in increased attention to sources and topics. Especially, social media sources and content that is self-referential to Reddit lead to high controversy in discussions.

Keyword‐Assisted Topic Models

AbstractIn recent years, fully automated content analysis based on probabilistic topic models has become popular among social scientists because of their scalability. However, researchers find that these models often fail to measure specific concepts of substantive interest by inadvertently creating multiple topics with similar content and combining distinct themes into a single topic. In this article, we empirically demonstrate that providing a small number of keywords can substantially enhance the measurement performance of topic models. An important advantage of the proposed keyword‐assisted topic model (keyATM) is that the specification of keywords requires researchers to label topics prior to fitting a model to the data. This contrasts with a widespread practice of post hoc topic interpretation and adjustments that compromises the objectivity of empirical findings. In our application, we find that keyATM provides more interpretable results, has better document classification performance, and is less sensitive to the number of topics.

The “computational turn”: an “interdisciplinary turn”? A systematic review of text as data approaches in journalism studies

Abstract Possibilities of applying automated content analysis in journalism studies include, for example, machine learning to identify topics in journalistic coverage or measuring news diffusion via automated approaches. But how have computational methods been applied thus far? And what are consequences of the “computational turn” in communication science, especially concerning interdisciplinarity? Based on a systematic literature review, this article summarizes the use of automated content analysis in journalism studies. Results illustrate an increasing use of the method by communication scientists, as yet another indicator of methodological interdisciplinarity in communication science. However, there is little evidence of an increase in theoretical interdisciplinarity: Studies relying on computational methods do not increasingly refer to theories from other disciplines. With respect to practical interdisciplinarity, for instance collaborations, our discipline is by no means becoming more interdisciplinary. Instead, we find a shift in favor of technical disciplines. At least up to now, the “computational turn” in communication science should thus not be equated with an “interdisciplinary turn.”

Pseudo-events: Tracking mediatization with machine learning over 40 years

Using automated content analysis, this research explores the phenomenon of pseudo-events coverage in The New York Times (N = 70,370 articles) from 1980 to 2019. By clarifying the operationalization of pseudo-events, this study introduces pseudo-events as a valuable tool to index how different social subsystems perpetuate mediatization (which is when institutions absorb and abide by media logic). Machine-learning classifiers were constructed to measure pseudo-events, which provides historicity, specificity, and measurability — three tasks set forth for new mediatization research. We found a significant increase in pseudo-event coverage, expressing a more positive tone than genuine event coverage. Moreover, political pseudo-event coverage shows quadrennial cycles with peaks in each presidential election year. Our findings reveal the expansion of mediatization since 1980 and show how media logic has been internalized in different ways by the social subsystems of politics, culture, and economics. Institutions and their social actors need efficient tools to abide by media logic in seeking publicity and commanding authority, and pseudo-events have matured into one of the most dominant tools, especially for political actors. This study offers an innovative approach to capture complex phenomena and shows promises of broader application of machine learning to empirically quantify and identify patterns using theoretical concepts.

The Not-So-Silent "Majority'': An Automated Content Analysis of Anti-Government Online Communities

The United States is facing an ongoing threat of political violence due to widespread anti-government sentiment that has proliferated across social media platforms. Most saliently, these violent sentiments manifested in the January 6, 2021, attack on the US Capitol during the certification of the 2020 general election. This research extends prior work on the online mobilisation to offline violence by analysing the text of online discussions leading up to the January 6 attack. We focus this examination on two central questions. First, what are the key themes and topics discussed within and across two social media platforms? Second, how did these themes and topics change over time? Focusing on two far-right anti-government online communities, we explore how support for political violence, disinformation, and electoral outcomes emerge and change over time. Our findings provide insight into possible strategies to counteract misinformation and the temporal trajectory of escalating violent sentiment within and across online communities. Further, this study highlights the importance of collecting data prospectively and demonstrates the value of automated content analysis and text data in understanding anti-government extremist sentiments.

Trends in grassland science: Based on the shift analysis of research themes since the early 1900s.

Grasslands, one of the major terrestrial ecosystems, are essential for the maintenance of ecological and production functions; however, they are undergoing extensive degradation. The development and cutting-edge explorations in grassland science are critical to addressing challenges such as climate change and the increasing influence of human activities. To identify research trends in grassland science, latent Dirichlet allocation (LDA) topic modelling was used to conduct an automated content analysis on 123,829 papers available on Web of Science Core Collection from 1900 to 2020. Results from this analysis showed that grassland research has become increasing multidisciplinary, accompanied by a pronounced reduction in the relative frequency of traditional production-oriented research and an increase in the themes focusing on ecological functions and modern technologies. Changes in research activities have been uneven globally, with a significant increase in the number of publications in China and Brazil, which probably reflects an increased support from various governmental agencies in these countries. Additionally, in 2019, China surpassed the United States in terms of the total number of publications. Further, this study identified important topics and emerging challenges in grassland research, such as biodiversity conservation, climate changes, and genetic considerations. Comprehensive improvement of education, research, global cooperation, and funding strategies will be necessary to promote grassland science research on frontier themes and to effectively address the social and environmental challenges in the new era.

Science governs the future of the mesopelagic zone

The potential of the mesopelagic zone (200–1000 m depth) to provide natural resources and ecosystem services is of increasing interest to a broad range of societal stakeholders. As this interest grows, divergent ideas about its current and future role in supporting human life are being expressed in scientific and public discourse. We use automated content analysis to identify 13 key topics about the mesopelagic zone from 2226 scientific abstracts and 4066 tweets. Two dominant ideas emerge: (1) the exploitation of fish resources and (2) the role of the mesopelagic zone as a carbon sink. We find that, in the absence of significant mesopelagic zone policy, science plays a de facto governing role by shaping how the ecosystem is known to the public and policymakers alike. These results indicate the need and potential for new knowledge and governance arrangements that can broaden the range of meanings and knowledge needed to anticipate future uses of deeply uncertain ecosystems and resources.

Challenging the Global Cultural Conflict Narrative: An Automated Content Analysis on How Perpetrator Identity Shapes Worldwide News Coverage of Islamist and Right-Wing Terror Attacks

Recent terrorist attacks such as the Christchurch mosque attacks in 2019 renew the discussion of whether right-wing attacks are reported less negatively than Islamist attacks. To clarify this point, our study is the first to combine the selection of media inside and outside the West with a distinction between Islamist and right-wing attacks. We compare coverage given to thirty-two right-wing and forty Islamist attacks from 2015 to 2019 in nine Western and eight non-Western English-language media outlets, tapping the differential use of the “terrorist/terrorism” label and textual sentiment. Both (many) Western and (some) non-Western media use this label more frequently in the coverage of Islamist attacks. Importantly, public diplomacy channels from non-Western countries such as China Daily and Sputnik also demonstrate this pattern. Delegitimizing Islamist attacks more than right-wing attacks thus cannot be explained as merely a Western phenomenon alone. We point to alternative explanations and call for greater standardization of coverage across Islamist and right-wing attacks.

Framing the climate crisis: Dread and fatalism in media and interest group responses to IPCC reports

AbstractWhile UN reports indicate increasingly dire consequences of climate change, the political will to initiate rapid decarbonization is lacking, as nations fail to meet targets set by international agreements. Given these developments, this paper investigates the role of climate dread and fatalism in the discourse about climate science. We examine the treatment of fatalism in major theories of the policy process, noting that climate policy represents a relatively novel situation—in which a problem once deemed solvable is being redefined as an issue outside the realm of human control. Using automated content analysis, we examine how journalists and interest groups framed reports issued by Intergovernmental Panel on Climate Change (IPCC) from 2013 to 2022. We find limited changes in the tone and content of news stories and environmental groups' statements, and we suggest that these findings reflect under‐reaction of the political system to new information.

Integrating Network Clustering Analysis and Computational Methods to Understand Communication With and About Brands: Opportunities and Challenges

Brand-related content cocreated by consumers can play a crucial role in brand–consumer interactions and provide brands with valuable insights hidden in vast seas of unstructured data. We propose and evaluate a framework integrating a social network approach and scalable automated content analysis of texts and visuals for studying brand-related communication on social media. To illustrate the proposed approach, we use Twitter content related to two brands: Barclays and Sierra Club. By applying network clustering algorithms we identify different types of organically emerging communities around brands. Cluster-specific diffusion leaders are identified using their in-degree centrality values. To examine the unique characteristics of brand-related content within each cluster, we apply and assess the accuracy of popular off-the-shelf solutions for text and image analysis, also known as application programming interfaces (APIs). Of six sentiment analysis solutions, only one shows acceptable reliability levels. For computer vision APIs, we first identify labels that have unclear or imprecise meaning and calculate accuracy levels, resulting in acceptable accuracy levels for four of the five APIs. We discuss conceptual and practical implications of this integrative approach and of the technological hurdles that these popular automated content analysis applications pose.

Visualizing environmental management: Corporate environmental images information disclosure and idiosyncratic risk

Enterprises pay more attention to the online visual communication of environmental information. Images significantly contribute to attracting the attention of stakeholders and enhancing the readability and understandability of information. This study collected 20,699 images of environmental information published on social media by environmentally sensitive Chinese enterprises from 2016 to 2020. Large-scale automatic image content analysis technology based on a computer vision algorithm was used to mine the disclosure characteristics of the environmental images. We further used a regression analysis to explore the effect of environmental images information disclosure on idiosyncratic risk. Our results show that enterprises are more willing to publish environmental management awareness images than renewable energy, environmental protection facilities, environmental certificates, and greening images. Enterprises with higher idiosyncratic risk and negative environmental events may disclose more environmental image information for impression management. The regression results show that environmental image disclosure can reduce idiosyncratic risk. However, this negative correlation no longer exists when idiosyncratic risk is high and negative environmental events occur. In conclusion, this study proposes a new perspective for examining the characteristics and effects of environmental information.

Smart contract diffusion in the pharmaceutical blockchain: the battle of counterfeit drugs

Counterfeit drugs have long been a major concern. In search of a solution, this study conducted a systematic literature review. Using an automated content analysis of pharmaceutical blockchains, this study identifies the (1) unique characteristics of smart contracts on blockchain platforms in the pharmaceutical supply chain, (2) role of smart contracts to combat counterfeit drugs, and (3) avenues for future studies. Theoretical and managerial implications are discussed at the end of this paper.

Covering the Crisis: Evolution of Key Topics and Actors in COVID-19 News Coverage in Switzerland

Objectives: The goal of this study is to map the share of COVID-related news articles over time, to investigate key subtopics and their evolution throughout the pandemic, and to identify key actors and their relationship with different aspects of the discourse around the pandemic.Methods: This study uses a large-scale automated content analysis to conduct a within-country comparison of news articles (N = 1,171,114) from two language regions of Switzerland during the first 18 months of the pandemic.Results: News media coverage of the pandemic largely mirrors key epidemiological developments in terms of the volume and content of coverage. Key actors in COVID-related reporting tend to be included in news articles that relate to their respective area of expertise.Conclusion: Balanced news coverage of the pandemic facilitates effective dissemination of pandemic-related information by health authorities.

Welfare state on the theoretical crossroads: analysis of the twenty-first-century studies

ABSTRACT Welfare theories, models, and programs vary in historical development and country. In the 21st century, socio-economic and economic changes significantly impact the welfare state and its social development and role. The aim of the paper is to identify the main focuses of welfare state research in the 21st century based on scientific articles. For this purpose, the tasks were set to implement bibliometric research, textual analysis, and systemic structuring results. The methodology covers bibliometric/authors co-occurrence analysis and automated content analysis, as well as general analysis and synthesis methods. Considering the dynamics and number of publications, the respective emphases of welfare state research are presented in three periods. First, bibliographic pairing analysis revealed clusters comprising conceptual nodes based on the keywords. After regrouping according to the close similarity enlarged 5 clusters presented in the paper are as follows: (1) welfare, (2) political issues, (3) service and care, (4) countries, and (5) women. The study will enable scientists and the interested public to gain an insight into current thematic and future research trends of the welfare state. Some interesting intersections for further research are suggested.

Financial performance and corporate risk disclosure: the moderating impact of board structure

The study aims to unravel the moderating impact of board attributes, i.e., board size, board independence and gender diversity on the relationship between firms' financial performance and corporate risk disclosure in the annual reports of Indian listed non-financial firms. For achieving the objective, the study deploys hierarchical moderated regression on a sample of S&P BSE-100 index pertaining to financial year 2018-2019. In addition, automated content analysis has been employed to operationalise the dependent variable, i.e., risk disclosure. The main findings unveil that board size and board independence positively moderate the relationship between firm performance and risk disclosure; suggesting that larger the board size and higher the proportion of independent directors; higher the performance impacts risk disclosure. Contrarily, proportion of women directors negatively moderates the relationship between firm performance and risk disclosure emphasising on the importance of women directors in disclosing risk in low profitable firms.

Automated content analysis of cultural Diversity Perspectives in Annual Reports (DivPAR): Development, validation, and future research agenda.

Objective: In this article, we present a digital tool (Diversity Perspectives in Annual Reports [DivPAR]) for automated content analysis of annual reports, designed to identify the presence of three cultural diversity perspectives-the Moral, Market, and Innovation perspectives-based on earlier work by Ely and Thomas (2001). Method: In Study 1, we describe the development and validation of the instrument, through an iterative procedure in which manual annotation of independent subsamples (n = 24, 25) by human coders was compared to the computer coding in subsequent rounds, until sufficient agreement was reached. In Study 2, we illustrate the type of data that the script generates, by analyzing the prevalence of the three perspectives in annual reports of 55 Dutch organizations over a period of 2 decades (1999-2018; n = 937). Results: Our findings confirm that DivPAR is sufficiently reliable for use in future research. In Study 2, we show that among Dutch organizations, the moral perspective is most prevalent, but the market and innovation perspectives are increasing in popularity. Conclusion: DivPAR can be used to analyze the prevalence and longitudinal development of diversity perspectives in organizational communication. It enables scholars to draw comparisons across different sectors, regions, or countries, to study how diversity perspectives correlate with societal developments, and to uncover the (lack of) relationships between diversity communication and diversity outcomes. Directions for future research are discussed at the end of the article. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Frame Repertoires at the Genre Level: An Automated Content Analysis of Character, Emotional, and Moral Framing in Satirical and Regular News

ABSTRACT Previous research suggests that, compared to regular news, satirical news has its own frame repertoire, which differs by outlet characteristics (e.g. medium type, political leaning). We analyzed two large corpora of satirical and regular news: television show episodes (8,925,180 words) and online written articles (64,301,669 words) of liberal and conservative outlets for character, emotional, and moral framing. Contrary to expectations, findings revealed consistent framing differences between the satirical and regular news genres, which were not moderated by medium type or political leaning. Thereby, this study shows how genre-level frame repertoires can offer deeper insights into cross-genre differences in news coverage.

Behind Blue Skies: A Multimodal Automated Content Analysis of Islamic Extremist Propaganda on Instagram

Social media platforms, such as Instagram, are regularly misused for spreading covert (Islamic) extremist propaganda. Affect and emotion are central tools used in extremist propaganda, but there is little research into the combined employment of different social media elements, such as hashtags, visuals, and texts, in the context of propaganda. This study contributes to closing this gap. Using the German group Generation Islam as a case study, we examined the group’s Instagram activity ( N = 1,187 posts) over the course of 2 years. To reflect the platform users’ logic, we (a) examined affect in hashtag networks in which users can come across propagandistic content, (b) employed deep learning to examine the emotional valence transmitted in the visuals, and (c) used automated linguistic analysis to describe collective action cues contained within the texts. The results are novel, as they provide nuanced insights into extremist propaganda’s employment of affect and emotions across Instagram’s affordances.