Semantic Network Analysis Research Articles

Constituting affective identities: Understanding the communicative construction of identity in online men’s rights spaces

The present study examined how identity is affectively organized online in an online men’s rights community, responding to calls to explore how sites like Reddit serve as spaces that host and support varied misogynist language and communities. We utilized scholarship of affective organizing and communicative constitution of organizations to study the identity construction through the social and communicative processes that facilitate and limit online communication. We analyzed 35,643 comments from a popular men’s rights community to interrogate how affective and gendered organizing contributed to identity construction using text mining, semantic network analyses, and qualitative analyses. Our findings revealed that affect was not merely prominent in the forum but served as a constitutive means through which the members of the men’s rights community constructed their identity individually and within their Reddit community. We advance an affect-centered approach within organizational communication scholarship, theorizing how masculinity is constituted through the interplay of affective contradictions, affective sensemaking, and affective identification.

Learning engagement in massive open online courses: A systematic review

Although massive open online courses (MOOCs) increase the number of choices in higher education and enhance learning, their low completion rate remains problematic. Previous studies have shown that learning engagement is a crucial factor influencing learning success and learner retention. However, few literature reviews on learning engagement in MOOCs have been conducted, and specific data analysis methods are lacking. Moreover, the internal and external factors that affect learning engagement have not been fully elucidated. Therefore, this systematic literature review summarized articles pertaining to learning engagement in MOOCs published from 2015 to 2022. Thirty articles met the inclusion and quality assurance criteria. We found that (1) learning engagement can be measured through analysis of log, text, image, interview, and survey data; (2) measures that have been used to analyze learning engagement include self-report (e.g., the Online Learning Engagement Scale, Online Student Engagement Questionnaire, and MOOC Engagement Scale) and automatic analysis methods [e.g., convolutional neural network (CNN), bidirectional encoder representations from transformers-CNN, K-means clustering, and semantic network analysis]; and (3) factors affecting learning engagement can be classified as internal (learning satisfaction, etc.) or external (curriculum design, etc.). Future research should obtain more diverse, multimodal data pertaining to social engagement. Second, researchers should employ automatic analysis methods to improve measurement accuracy. Finally, course instructors should provide technical support (“scaffolding”) for self-regulated learning to enhance student engagement with MOOCs.

Forecasting financial markets with semantic network analysis in the COVID‐19 crisis

AbstractThis paper uses a new textual data index for predicting stock market data. The index is applied to a large set of news to evaluate the importance of one or more general economic‐related keywords appearing in the text. The index assesses the importance of the economic‐related keywords, based on their frequency of use and semantic network position. We apply it to the Italian press and construct indices to predict Italian stock and bond market returns and volatilities in a recent sample period, including the COVID‐19 crisis. The evidence shows that the index captures the different phases of financial time series well. Moreover, results indicate strong evidence of predictability for bond market data, both returns and volatilities, short and long maturities, and stock market volatility.

A broad approach to expert detection using syntactic and semantic social networks analysis in the context of Global Software Development

Social network analysis has been widely used in different application contexts. For example, in Global Software Development, where multiple developers with diverse skills and knowledge are involved, the use of social networking models helps to understand how these developers collaborate. Finding experts who can help address critical elements or issues in a project is a challenging and critical task. It is especially true in the context of Global Software Development projects, where developers with specific skills and knowledge often need to be identified. In this sense, searching for essential members is a valuable task, as they are fundamental to the evolution of the network. This article proposes a broad solution for syntactic and semantic analysis in social networks in the Global Software Development context. In this solution, we define a model for the social network capable of capturing collaboration between developers, incorporate strategies for temporal analysis of the network, explore the network using machine learning algorithms, and propose an ontology to enrich the data semantically. We conducted three case studies using data extracted from GitHub to evaluate the proposed approach. The case studies provide evidence that our proposed method can identify specialists, highlighting their expertise and importance to the evolution of the social network.

Toward a Decolonial Parenting Science Through Centering Majority World Parenting: A Commentary on “Parenting Culture(s): Ideal-Parent Beliefs Across 37 Countries”

In “Parenting Culture(s): Ideal-Parent Beliefs Across 37 Countries,” Lin et al. examine reports of parents from 37 countries regarding the qualities they consider in an ideal parent and then use a method called Leximancer Semantic Network Analysis to identify broad culture zones across the 37 countries based on shared notions of an ideal parent. I appreciate this substantial effort to explore parenting beyond Euro-American samples, upon which a bulk of parenting science literature is based. I also concur with a data-driven exploratory approach and examination of ideal-parent beliefs across parents with differing educational levels. However, I argue that to advance parenting science, we need more than inclusion of samples from the Majority World (i.e., regions where the majority of the world’s population resides). Future parenting research should be grounded in (a) decolonial epistemology that involves generating localized knowledge by exploring parenting in various communities in the Majority World that are formed through intersecting influences of neighborhood composition, religion, region, social class, urban, rural, and suburban residence, along with other locally relevant social dimensions, (b) decolonial research methodology that values different ways of generating knowledge and includes local communities as partners in the knowledge generation process, and (c) understanding and interpreting parenting in Majority World communities from a cultural resource rather than a deficit framework.

Exploring 19th-century Perception of “Uncanny” Music Using a Semantic Network Approach

The perception and experience of emotions in response to music listening are subject of a growing body of empirical research across the humanities and social sciences. While we are now able to investigate music perception in different parts of the world, insights into historical music perception remain elusive, mainly because the direct interrogation of music listeners of the past is no longer possible. Here, we present an approach to the retroactive exploration of historical music perception using semantic network analysis of historical text documents. To illustrate this approach, we analyzed written accounts of 19th-century perception of music that is described as “uncanny” (unheimlich). The high centrality values of “eerie” (gespenstisch) indicate that music termed as such should be highly similar to “uncanny” (unheimlich) music. We thus also analyzed written accounts of 19th-century perception of music described as “eerie” (gespenstisch). Using semantic network analyses on other expressive qualities as well as compositional features, we were then able to highlight in which way “uncanny” (unheimlich) and “eerie” (gespenstisch) music are similar and how they might be distinguished. Semantic network analysis may thus be a valuable tool in describing what compositional features were associated with particular expressive qualities by listeners of the past.

The Ontology of Work during Pandemic Lockdown: A Semantic Network Analytical Approach

Digital nomadism is emerging as a growing segment of the labor force. It is an insightful framework for understanding work during the pandemic and perhaps into the post-pandemic era because it construes work to be related to the notion of space, time and the instrumentality of work. The present study is about how people understand, relate, and make sense of their work during the early phase of the pandemic lockdown in 2020. The study will report difficulties that arise from work digitalization during the lockdown, and the study conceived the various dimensionality of work to cope with work challenges. Semantic network analysis (SNA) was used to aid the analysis of the contents from four European countries. One hundred and sixty respondents are interviewed using a semi-structured questionnaire. The words and word pairs from the SNA resulted in keywords identified for the four countries. There are common word hubs between the countries, such as hubs revolving around the meaning of ‘time’ and ‘meeting’. However, there are also unique hubs such as ‘task’, ‘office’ and “colleagues”. The results provide a cross-cultural comparison of how people adopted to work change. The organization of the word pairs in the network provided the narratives.

Supplemental Material for Legacies and last words: Exploring expressed experiences of robot death.

As robots are increasingly designed and marketed for social adoption, questions emerge about manufacturers’ ethical responsibilities for ensuring continuity of their creations. However, addressing such questions first requires advancing empirical understandings of how humans experience robots’ metaphorical death (i.e., functional cessation). This study advances that aim by exploring an interstitial: public reactions to a known robot’s functional cessation (RFC). Focusing on a specific RFC event—the official announced demise of the “Opportunity” Mars rover—this study unpacked experience dimensions by inductively analyzing public tweets about the event. Semantic network analysis of N = 10,303 tweets is interpreted as indicating 11 key expressed themes (robot legacy, journey, last words, persistence, contributions, and death finality; physiological and emotional reactions; formal and meme-based salutations and memorializing). Interpretation of network content suggests three potential overarching orientations toward the RFC event: cessation as death, as milestone, and as material loss.

Legacies and last words: Exploring expressed experiences of robot death.

As robots are increasingly designed and marketed for social adoption, questions emerge about manufacturers’ ethical responsibilities for ensuring continuity of their creations. However, addressing such questions first requires advancing empirical understandings of how humans experience robots’ metaphorical death (i.e., functional cessation). This study advances that aim by exploring an interstitial: public reactions to a known robot’s functional cessation (RFC). Focusing on a specific RFC event—the official announced demise of the “Opportunity” Mars rover—this study unpacked experience dimensions by inductively analyzing public tweets about the event. Semantic network analysis of N = 10,303 tweets is interpreted as indicating 11 key expressed themes (robot legacy, journey, last words, persistence, contributions, and death finality; physiological and emotional reactions; formal and meme-based salutations and memorializing). Interpretation of network content suggests three potential overarching orientations toward the RFC event: cessation as death, as milestone, and as material loss.

What Do Employers Expect for Jobs Requiring Media Analytics? A Semantic Network Analysis of Job Descriptions of In-Person and Remote Positions During the COVID-19 Pandemic

Using text mining and semantic network analysis, this study analyzed the job descriptions of 34,787 positions about media analytics from Indeed.com to compare how the in-person and remote jobs differ to inform educators about integrating analytics in the media and communications curriculum. We found that the in-person positions emphasized more on the skills of verbal, interpersonal, and organizational communication, whereas the remote positions asked more for written communication. While the in-person positions had higher expectations of using general data management and analysis tools, the remote positions emphasized more on the use of social media analytics and digital marketing tools.

Semantic network analysis on the geography of food as represented in textbooks: for alternative food education for young people

Semantic network analysis on the geography of food as represented in textbooks: for alternative food education for young people

Ecosystem Services: A Social and Semantic Network Analysis of Public Opinion on Twitter.

Social media data reveal patterns of knowledge, attitudes, and behaviours of users on a range of topics. This study analysed 4398 tweets gathered between 17 January 2022 and 3 February 2022 related to ecosystem services, using the keyword and hashtag "ecosystem services". The Microsoft Excel plugin, NodeXL was used for social and semantic network analysis. The results reveal a loosely dense network in which information is conveyed slowly, with homogeneous, medium-sized subgroups typical of the community cluster structure. Citizens, NGOs, and governmental administrations emerged as the main gatekeepers of information in the network. Various semantic themes emerged such as the protection of natural capital for the sustainable production of ecosystem services; nature-based solutions to protect human structures and wellbeing against natural hazards; socio-ecological systems as the interaction between human beings and the environment; focus on specific services such as the storage of atmospheric CO2 and the provision of food. In conclusion, the perception of social users of the role of ecosystem services can help policymakers and forest managers to outline and implement efficient forest management strategies and plans.

Parenting Culture(s): Ideal-Parent Beliefs Across 37 Countries

What is it to be “an ideal parent”? Does the answer differ across countries and social classes? To answer these questions in a way that minimizes bias and ethnocentrism, we used open-ended questions to explore ideal-parent beliefs among 8,357 mothers and 3,517 fathers from 37 countries. Leximancer Semantic Network Analysis was utilized to first determine parenting culture zones (i.e., countries with shared ideal-parent beliefs) and then extract the predominant themes and concepts in each culture zone. The results yielded specific types of ideal-parent beliefs in five parenting culture zones: being “responsible and children/family-focused” for Asian parents, being “responsible and proper demeanor-focused” for African parents, and being “loving and responsible” for Hispanic-Italian parents. Although the most important themes and concepts were the same in the final two zones—being “loving and patient,” there were subtle differences: English-speaking, European Union, and Russian parents emphasized “being caring,” while French-speaking parents valued “listening” or being “present.” Ideal-parent beliefs also differed by education levels within culture zones, but no general pattern was discerned across culture zones. These findings suggest that the country in which parents were born cannot fully explain their differences in ideal-parent beliefs and that differences arising from social class or education level cannot be dismissed. Future research should consider how these differences affect the validity of the measurements in question and how they can be incorporated into parenting intervention research within and across cultures.

Sharing Economy of Online Food Delivery: Revealing the Underlying Attributes of the Customer Experience through Semantic Network Analysis

Research Aims: This study aimed to reveal the underlying attributes of the customer experience in the sharing economy of online food delivery (OFD). Design/Methodology/Approach: After collecting 45,116 reviews from the Google Play store, a semantic network analysis was conducted. Python programming language and text mining were utilised to extract keywords from online reviews, a frequency analysis was performed, and a CONvergence of iterated CORrelations (CONCOR) analysis was conducted using Ucinet 6.0. Research Findings: The keywords ‘food,’ ‘order,’ ‘driver,’ and ‘application’ had the highest frequency and centrality. Customer experience attributes were classified into four clusters: ‘Delivery Procedure’, ‘OFD Platform’, ‘Payment Process’ and ‘Value of Money’. Theoretical Contribution/Originality: This study provides a relevant and novel assessment of customer experience using semantic network analysis, which should be more broadly used in academic research. Managerial Implications in the Southeast Asian Context: Given the predicted growth of online food delivery in Southeast Asia and shared cultural values with Indonesia, the findings of this study may have implications for developing strategies of sustainability in the sharing economy of online food delivery enterprises. Research Limitations & Implications: This study only collected online customer reviews from the Google Play store, and because the method focused on word frequency, understanding of the additional meaning of words is lacking.

공유가치창출(Creating Shared Value)에 대한 뉴스기사와 블로그의 프레임 및 네트워크 의제 설정 연구: 토픽모델링과 의미연결망을 중심으로

This study attempted to examine through what content structure keywords related to CSV have been delivered to consumers for 10 years from 2011 to September 2021. As a result of topic modeling related to CSV, similar concept confusion frames, CSV development necessity frames, and discussion necessity frames were common in news articles and blogs. However, in the case of news articles, there was a difference in cooperation and support for small and medium-sized enterprises, solving problems for the socially under-privileged, a win-win frame for local communities, and a strategy frame for securing competitiveness for blogs. On the other hand, as a result of examining the four major centrality indicators of news articles and blogs related to CSV through semantic network analysis, it was possible to guess the similarity of semantic network between news articles and blogs. Finally, as a result of QAP correlation analysis between news articles and blogs, it was found that there was a statistically significant positive correlation between the news article agenda and the blog agenda. In addition, as a result of MR-QAP regression analysis, it was confirmed that the agenda shown in the news article also affects the agenda shown in the blogs.

빅데이터를 활용한 코로나19 시기의 홈트레이닝 발전경향 분석

Purpose: This study analyzes big data to suggest the development tendency and marketing strategies of home training, which is growing rapidly due to the COVID-19 that started in 2020.BR Method: To this end, text-mining, tf-idf, connection degree centrality and semantic network analysis were conducted by using textom, a social matrix program, and Ucinet for network analysis. The data analysis period was selected for six quarters from January 1, 2020 to June 30, 2021.BR Results: The main analysis results of the study were as follows. First, home training’s word frequency, tf-idf, and connection degree centrality analysis quarterly showed that exercise, home training, home workout, and diet words were always in the top 10, but the bottom six words in the six quarters showed different tendencies. This shows that the perception of home training varies by quarter during the COVID-19 period. Second, as a result of quarterly home training semantic network analysis, the word composition of both the home training and home training trend groups were different. This shows that home training showed different development trends for each quarter during the COVID-19 period.BR Conclusion: Therefore, the purpose is to provide practical data based on the analyzed results, and to present a strategy to promote the development of the home training market at the theoretical level.

A study on the Improvement of the General Education Curriculum Based on Analysis of the Learning Objective

Competency-based education focuses on the question, “What can be done?” Therefore, it is necessary to educate learners so that they can perform what they know in the context of life. In addition, it is necessary to present specific learning objectives so that students can understand the content of the subject and use their knowledge to acquire the competencies expressed in specific situations. Therefore, in this study the actual condition of the learning objective of the lesson plans for general education subjects was identified. Also, I tried to propose systematic improvement measures and action strategies for the effective implementation of the general education curriculum. To this end, semantic network analysis was conducted by collecting the lesson plans of 138 subjects in the general education curriculum of University A.As a result, ‘understanding’, ‘acquisition’, and ‘cultivation’ appeared as the keywords of the general education curriculum learning objectives. However, I found that it was insufficient to present practical and specific learning objectives that are characteristic of the competency-based curriculum. Based on these results, I propose that we review the learning objectives system, strengthen the instructor's competency, and establish a goal-based curriculum system to operate the competency-based general education curriculum.

텍스트 마이닝 분석을 이용한 아파트 광고 메시지 연구

This study conducted an analysis with the semantic network analysis technique to figure out house values sought after in the messages of local apartment sales advertisements. The study collected analysis data from the messages of 54 apartment sales advertisements in total published in B, which is one of the most representative newspapers in Busan and has the most subscribers, for two years of June 2020~June 2022. The analysis focus was put on the headlines, subheadlines, body copies, and captions of newspaper ads. R was used in a text mining analysis including the analysis of word clouds and semantic networks as well as frequency analysis to identify the housing value factors conveyed to consumers exposed to these advertising messages. The main findings were summarized as follows: first, such keywords as

Effects of selfie semantic network analysis and AR camera app use on appearance satisfaction and self-esteem

Effects of selfie semantic network analysis and AR camera app use on appearance satisfaction and self-esteem

Comparative Evaluation of Mountain Landscapes in Beijing Based on Social Media Data

An important part of Beijing’s ecological pattern, mountain landscapes are also the most important natural tourist destinations in Beijing. The unique mountain environment in Taihang and Yan Mountains attracts Beijing and foreign tourists alike. Tourists publish travel photos and comments on social media, which provides a new opportunity for a systematic evaluation of these mountain parks based on social media data. To fully understand the developmental status of mountain landscapes in Beijing, this paper comparatively evaluates 45 mountain landscapes in Beijing based on social media data. Using big data capture, semantic network analysis, importance-performance analysis (IPA), etc., it explores the composition of tourist groups in mountain parks, the preferences of the tourist groups, and the relationships between park tourists and different influencing factors, and evaluates the recreational experiences of tourist groups. The development of recreational activities was found to be more important to local tourists than scenic sites for foreign tourists. According to gender differences, women were more interested in recreational experiences than men, while men were more interested in the park’s landscapes. According to the IPA, tourists were satisfied with the overall recreation offered by mountain landscapes. The perceptual experience was dominated by visual perception, followed by smell; touch, hearing, and taste were of minor importance. Using social media data to analyze mountain landscape resources in Beijing can provide useful insights into the advantages of these landscapes under a variety of site conditions, strengthen local mountain resource development and tourism publicity, integrate tourism management and planning resources in a targeted and attractive manner, and enhance ecological leisure services.