Pub Date : 2025-09-13DOI: 10.1016/j.dim.2025.100111
Lin Li , Wei Chen , Gaohui Cao
The rise of cyberchondria has become a troubling side effect of the digital age, drawing concern due to its negative psychological impact. This study investigates the link between excessive social media use for health information and the development of cyberchondria. The current research focuses on the environmental and emotional stimuli, and social media communication overload as organism to examine the mechanism of cyberchondria. The findings suggest that increasing engagement with online health resources is associated with a reduction in information and communication overload. Conversely, heightened levels of fear of missing out can exacerbate these overloads. As information and communication overload escalate, so does cyberchondria. The significance of our findings lies in our expansion of the SOR model through the assessment of these factors in relation to the development of cyberchondria.
{"title":"How online health information exposure and fear of missing out drive Cyberchondria? The dual-stimulus effect","authors":"Lin Li , Wei Chen , Gaohui Cao","doi":"10.1016/j.dim.2025.100111","DOIUrl":"10.1016/j.dim.2025.100111","url":null,"abstract":"<div><div>The rise of cyberchondria has become a troubling side effect of the digital age, drawing concern due to its negative psychological impact. This study investigates the link between excessive social media use for health information and the development of cyberchondria. The current research focuses on the environmental and emotional stimuli, and social media communication overload as organism to examine the mechanism of cyberchondria. The findings suggest that increasing engagement with online health resources is associated with a reduction in information and communication overload. Conversely, heightened levels of fear of missing out can exacerbate these overloads. As information and communication overload escalate, so does cyberchondria. The significance of our findings lies in our expansion of the SOR model through the assessment of these factors in relation to the development of cyberchondria.</div></div>","PeriodicalId":72769,"journal":{"name":"Data and information management","volume":"10 1","pages":"Article 100111"},"PeriodicalIF":0.0,"publicationDate":"2025-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145476084","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The emergence of virtual communities through social and online media has raised concerns regarding the dissemination of misinformation and its local and global impact on socioeconomic and political changes. Although numerous studies have been conducted on this topic in other domains, the extent to which misinformation affects the agri-food industry remains largely unexplored. This research aimed to fill this gap by investigating the prevalence and impact of misinformation in two popular Sri Lankan virtual communities of practice (VCoPs): Krushi Arunodaya and Turmeric, Ginger, Pepper & Cinnamon Cultivators’ and Buyers’ Association. Through qualitative research consisting of 16 key information interviews with group administrators and members, the study discovered that agricultural misinformation is rampant in Sri Lankan agri-food VCoPs, polarizing members on crucial topics such as organic farming, GMOs, and chemical fertilizers. The perception of misinformation and its dissemination is influenced by cultural, political, and societal factors, as well as individual personality traits and the need for self-expression. However, those with media literacy, knowledge, and experience are better suited to identify and avoid misinformation. The research also found that traditional media is involved in promoting agenda-based campaigns alongside social media and internet-based platforms. VCoP members recommended reporting and blocking as primary countermeasures to combat misinformation. Multi-stakeholder interventions by government, media, agricultural organizations, and VCoP moderators are necessary to prevent agri-food misinformation in Sri Lanka. Additionally, media agencies and experts should act responsibly in disseminating accurate information.
{"title":"Evils of knowledge sharing and learning: The case of agri-food misinformation in virtual communities of practices in Sri Lanka","authors":"Kasuni Sachithra Illesinghe Kankanamge, Ataharul Chowdhury, Khondokar Humayun Kabir, Nasir Abbas Khan","doi":"10.1016/j.dim.2024.100090","DOIUrl":"10.1016/j.dim.2024.100090","url":null,"abstract":"<div><div>The emergence of virtual communities through social and online media has raised concerns regarding the dissemination of misinformation and its local and global impact on socioeconomic and political changes. Although numerous studies have been conducted on this topic in other domains, the extent to which misinformation affects the agri-food industry remains largely unexplored. This research aimed to fill this gap by investigating the prevalence and impact of misinformation in two popular Sri Lankan virtual communities of practice (VCoPs): Krushi Arunodaya and Turmeric, Ginger, Pepper & Cinnamon Cultivators’ and Buyers’ Association. Through qualitative research consisting of 16 key information interviews with group administrators and members, the study discovered that agricultural misinformation is rampant in Sri Lankan agri-food VCoPs, polarizing members on crucial topics such as organic farming, GMOs, and chemical fertilizers. The perception of misinformation and its dissemination is influenced by cultural, political, and societal factors, as well as individual personality traits and the need for self-expression. However, those with media literacy, knowledge, and experience are better suited to identify and avoid misinformation. The research also found that traditional media is involved in promoting agenda-based campaigns alongside social media and internet-based platforms. VCoP members recommended reporting and blocking as primary countermeasures to combat misinformation. Multi-stakeholder interventions by government, media, agricultural organizations, and VCoP moderators are necessary to prevent agri-food misinformation in Sri Lanka. Additionally, media agencies and experts should act responsibly in disseminating accurate information.</div></div>","PeriodicalId":72769,"journal":{"name":"Data and information management","volume":"9 3","pages":"Article 100090"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144919999","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-01DOI: 10.1016/j.dim.2024.100091
Xiling Cui , Xuan Yang , Jifan Ren , Paul Benjamin Lowry
{"title":"Corrigendum to “Enhancing team creativity among information technology professionals through knowledge sharing and motivational rewards: A self-determination perspective” [Data and Information Management 9/2 (2025) 100075]","authors":"Xiling Cui , Xuan Yang , Jifan Ren , Paul Benjamin Lowry","doi":"10.1016/j.dim.2024.100091","DOIUrl":"10.1016/j.dim.2024.100091","url":null,"abstract":"","PeriodicalId":72769,"journal":{"name":"Data and information management","volume":"9 3","pages":"Article 100091"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144920000","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-01DOI: 10.1016/j.dim.2024.100086
Jiang Wu , Zhoucan Xu , Qian Huang , Jingxuan Cai
The Online Q&A community provides platforms for Internet users to exchange and share knowledge. Its rapid development has caused the problem of information overload, which promotes users' demand for precise and personalized information. In light of this, we innovatively analyze the correlation between knowledge quality and user behaviors. First, we propose a novel approach method to evaluate the answer quality through a machine learning approach, which applies the information adoption theory to the design of a text classification model. Hereafter, with applications of motivation crowding theory, this paper introduces the classification results of answer quality by machine learning algorithms into the empirical research model to explore the factors that motivate users in both participation and high-quality user-generated content creation. Results show that both extrinsic and intrinsic factors determine the quality of knowledge contributors' answers. Further, certain external interventions (monetary rewards) can crowd out the effects of knowledge contributors’ intrinsic motivations (knowledge self-efficacy). It enriches the research on user knowledge contribution behavior in online Q&A communities and also provides theoretical guidance and suggestions for community operation.
{"title":"What motivates knowledge sharing? Evaluating the quality of answer contribution in online Q&A communities","authors":"Jiang Wu , Zhoucan Xu , Qian Huang , Jingxuan Cai","doi":"10.1016/j.dim.2024.100086","DOIUrl":"10.1016/j.dim.2024.100086","url":null,"abstract":"<div><div>The Online Q&A community provides platforms for Internet users to exchange and share knowledge. Its rapid development has caused the problem of information overload, which promotes users' demand for precise and personalized information. In light of this, we innovatively analyze the correlation between knowledge quality and user behaviors. First, we propose a novel approach method to evaluate the answer quality through a machine learning approach, which applies the information adoption theory to the design of a text classification model. Hereafter, with applications of motivation crowding theory, this paper introduces the classification results of answer quality by machine learning algorithms into the empirical research model to explore the factors that motivate users in both participation and high-quality user-generated content creation. Results show that both extrinsic and intrinsic factors determine the quality of knowledge contributors' answers. Further, certain external interventions (monetary rewards) can crowd out the effects of knowledge contributors’ intrinsic motivations (knowledge self-efficacy). It enriches the research on user knowledge contribution behavior in online Q&A communities and also provides theoretical guidance and suggestions for community operation.</div></div>","PeriodicalId":72769,"journal":{"name":"Data and information management","volume":"9 3","pages":"Article 100086"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144920198","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-01DOI: 10.1016/j.dim.2024.100088
Ahmet Toprak, Metin Turan
Dictionary is helpful tool for most of the context-based Natural Language Processing researches. The words in the language dictionary establish the context coverage for a specific application area. In the study, a novel model is proposed to generate thematic dictionary using the web resources. The model gets the benefit of different text similarity algorithms to enhance dictionary coverage and increase its internal similarity. For example, in order to create a financial dictionary, algorithm was started with a general seed word “finance”. Web search was executed with this word, and the top three web pages returned by the web search engine were processed. The words in the contents of these web pages were ranked according to their meaning values using the term frequency-inverse document frequency metric. Then, selected words were initially inserted into three different dictionaries which were controlled by WordNet, Spacy, and Simhash text similarity algorithms separately. All of these words added into these dictionaries were used for further web search again together. This process (search and dictionary update) of the algorithm was repeated for each dictionary separately until each reaches to the upper count of words (250 words have been set). Finally, these three dictionaries are merged to form the final financial dictionary. This financial dictionary was compared with the manually created financial dictionary in terms of quality. Consequently, the internal WordNet similarity rate of the words in the automatic financial dictionary was 29.01%, while it was 23.41% in the manual financial dictionary. For the similarity measure of both dictionaries, when the words were merged in the automatic and manual dictionaries into full texts and evaluated both in terms of Simhash similarity, then 72.30% similarity was obtained. It was seen that although both dictionaries produce almost similar words, the automatic dictionary had stronger internal semantic representation.
{"title":"Automated thematic dictionary creation using the web based on WordNet, Spacy, and Simhash","authors":"Ahmet Toprak, Metin Turan","doi":"10.1016/j.dim.2024.100088","DOIUrl":"10.1016/j.dim.2024.100088","url":null,"abstract":"<div><div>Dictionary is helpful tool for most of the context-based Natural Language Processing researches. The words in the language dictionary establish the context coverage for a specific application area. In the study, a novel model is proposed to generate thematic dictionary using the web resources. The model gets the benefit of different text similarity algorithms to enhance dictionary coverage and increase its internal similarity. For example, in order to create a financial dictionary, algorithm was started with a general seed word “finance”. Web search was executed with this word, and the top three web pages returned by the web search engine were processed. The words in the contents of these web pages were ranked according to their meaning values using the term frequency-inverse document frequency metric. Then, selected words were initially inserted into three different dictionaries which were controlled by WordNet, Spacy, and Simhash text similarity algorithms separately. All of these words added into these dictionaries were used for further web search again together. This process (search and dictionary update) of the algorithm was repeated for each dictionary separately until each reaches to the upper count of words (250 words have been set). Finally, these three dictionaries are merged to form the final financial dictionary. This financial dictionary was compared with the manually created financial dictionary in terms of quality. Consequently, the internal WordNet similarity rate of the words in the automatic financial dictionary was 29.01%, while it was 23.41% in the manual financial dictionary. For the similarity measure of both dictionaries, when the words were merged in the automatic and manual dictionaries into full texts and evaluated both in terms of Simhash similarity, then 72.30% similarity was obtained. It was seen that although both dictionaries produce almost similar words, the automatic dictionary had stronger internal semantic representation.</div></div>","PeriodicalId":72769,"journal":{"name":"Data and information management","volume":"9 3","pages":"Article 100088"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144920200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-01DOI: 10.1016/j.dim.2024.100092
Manika Lamba , Hendrik Erz
Research on acknowledgment sections of scientific papers has gained significant attention, but there remains a dearth of studies examining acknowledgments in the context of Electronic Theses and Dissertations (ETDs). This paper addresses this gap by investigating the sources of support for male and female researchers in completing their master's or doctoral theses, focusing on the discipline of Library and Information Science (LIS). We utilize a novel method of extracting the various types of support systems that are acknowledged in 1252 ETDs using BERT models. The most prominent forms of support acknowledged by researchers are academic, moral, financial, and religious support. While there were no significant gender-based differences in religious and financial support, the ratio of academic to moral support acknowledged by researchers showed strong gender-based variation. Additionally, advisors displayed a preference for supervising same-gender researchers. By comprehending the nuances of support systems and the unique challenges faced by researchers of different genders, we can foster a more inclusive and supportive academic environment. The insights gained from this research have implications for improving mentoring practices and promoting gender equality in academia.
{"title":"Thanking the World: Exploring gender-based differences in acknowledgment patterns and support systems in theses","authors":"Manika Lamba , Hendrik Erz","doi":"10.1016/j.dim.2024.100092","DOIUrl":"10.1016/j.dim.2024.100092","url":null,"abstract":"<div><div>Research on acknowledgment sections of scientific papers has gained significant attention, but there remains a dearth of studies examining acknowledgments in the context of Electronic Theses and Dissertations (ETDs). This paper addresses this gap by investigating the sources of support for male and female researchers in completing their master's or doctoral theses, focusing on the discipline of Library and Information Science (LIS). We utilize a novel method of extracting the various types of support systems that are acknowledged in 1252 ETDs using BERT models. The most prominent forms of support acknowledged by researchers are academic, moral, financial, and religious support. While there were no significant gender-based differences in religious and financial support, the ratio of academic to moral support acknowledged by researchers showed strong gender-based variation. Additionally, advisors displayed a preference for supervising same-gender researchers. By comprehending the nuances of support systems and the unique challenges faced by researchers of different genders, we can foster a more inclusive and supportive academic environment. The insights gained from this research have implications for improving mentoring practices and promoting gender equality in academia.</div></div>","PeriodicalId":72769,"journal":{"name":"Data and information management","volume":"9 3","pages":"Article 100092"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144920199","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-01DOI: 10.1016/j.dim.2025.100094
Xiaohui Liu, Guiyan Ou, Chuanfu Chen
Interdisciplinary research (IDR) has been highly encouraged in modern science as it is believed to bring forth scientific breakthroughs. However, it is widely believed that engaging in IDR may not favorably impact a researcher's career development. This study seeks to explore whether top researchers encounter similar pessimistic outlooks when participating in IDR. Accordingly, this study focuses on funded applicants, who presumably demonstrate a degree of excellence above those who were not awarded funding to some extent. Specifically, our study was designed to explore the impact of interdisciplinarity on individual-sponsored funding based on the dataset of the National Natural Science Foundation of China (NSFC). Our data set obtained from the NSFC comprises 224,085 records of funded projects between 1999 and 2014, encompassing all scientific departments from the NSFC system. We analyzed the relationship between the researcher's interdisciplinarity and their funding performance. Moreover, we examined the influential role that various factors played in moderating the link between interdisciplinarity and individual-sponsored funding such as the experience of granter, affiliation reputation, and affiliation disciplinary advantage. The results showed a positive effect of interdisciplinarity on researchers' funding performance. Individuals with a higher degree of interdisciplinarity tended to receive a greater number of funded projects and higher funding values. Additionally, the experience of granter, affiliation reputation, and affiliation disciplinary advantage all played a positive moderating role in this relationship. This study fills essential lacunae in our understanding of support systems for interdisciplinary research within China's grant-giving framework. It further provides significant insights into the connection between interdisciplinary researchers and their funding outcomes.
{"title":"Good to great: The impact of interdisciplinarity on the researchers’ funding performance","authors":"Xiaohui Liu, Guiyan Ou, Chuanfu Chen","doi":"10.1016/j.dim.2025.100094","DOIUrl":"10.1016/j.dim.2025.100094","url":null,"abstract":"<div><div>Interdisciplinary research (IDR) has been highly encouraged in modern science as it is believed to bring forth scientific breakthroughs. However, it is widely believed that engaging in IDR may not favorably impact a researcher's career development. This study seeks to explore whether top researchers encounter similar pessimistic outlooks when participating in IDR. Accordingly, this study focuses on funded applicants, who presumably demonstrate a degree of excellence above those who were not awarded funding to some extent. Specifically, our study was designed to explore the impact of interdisciplinarity on individual-sponsored funding based on the dataset of the National Natural Science Foundation of China (NSFC). Our data set obtained from the NSFC comprises 224,085 records of funded projects between 1999 and 2014, encompassing all scientific departments from the NSFC system. We analyzed the relationship between the researcher's interdisciplinarity and their funding performance. Moreover, we examined the influential role that various factors played in moderating the link between interdisciplinarity and individual-sponsored funding such as the experience of granter, affiliation reputation, and affiliation disciplinary advantage. The results showed a positive effect of interdisciplinarity on researchers' funding performance. Individuals with a higher degree of interdisciplinarity tended to receive a greater number of funded projects and higher funding values. Additionally, the experience of granter, affiliation reputation, and affiliation disciplinary advantage all played a positive moderating role in this relationship. This study fills essential lacunae in our understanding of support systems for interdisciplinary research within China's grant-giving framework. It further provides significant insights into the connection between interdisciplinary researchers and their funding outcomes.</div></div>","PeriodicalId":72769,"journal":{"name":"Data and information management","volume":"9 3","pages":"Article 100094"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144920212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper presents the implementation and evaluation of the Data Capsule framework, a novel approach for achieving personal data sovereignty. Our framework uses formal knowledge representation to understand both the context of personal data collection across heterogeneous systems and define comprehensive usage policies - from access control to monetisation opportunities. As organisations increasingly collect and process personal data, individuals continue to lack effective mechanisms to control how their information is processed and/or shared across heterogeneous systems. We tackle this problem with two key contributions: (1) an ontology-based federation system that allows for seamless federation of personal data across databases using schema.org as a semantic foundation, and (2) a semantically driven dynamic usage control mechanism that allows individuals to define and enforce granular access rules. Our implementation demonstrates that effective personal data sovereignty can be achieved and serves as a foundation for future systems contributing to the empowerment of individuals in the digital economy.
{"title":"Defining personal data Sovereignty: An ontologically-based framework facilitating subject privacy control","authors":"Vijon Baraku , Edon Ramadani , Iraklis Paraskakis , Simeon Veloudis , Poonam Yadav","doi":"10.1016/j.dim.2025.100108","DOIUrl":"10.1016/j.dim.2025.100108","url":null,"abstract":"<div><div>This paper presents the implementation and evaluation of the Data Capsule framework, a novel approach for achieving personal data sovereignty. Our framework uses formal knowledge representation to understand both the context of personal data collection across heterogeneous systems and define comprehensive usage policies - from access control to monetisation opportunities. As organisations increasingly collect and process personal data, individuals continue to lack effective mechanisms to control how their information is processed and/or shared across heterogeneous systems. We tackle this problem with two key contributions: (1) an ontology-based federation system that allows for seamless federation of personal data across databases using <span><span>schema.org</span><svg><path></path></svg></span> as a semantic foundation, and (2) a semantically driven dynamic usage control mechanism that allows individuals to define and enforce granular access rules. Our implementation demonstrates that effective personal data sovereignty can be achieved and serves as a foundation for future systems contributing to the empowerment of individuals in the digital economy.</div></div>","PeriodicalId":72769,"journal":{"name":"Data and information management","volume":"10 1","pages":"Article 100108"},"PeriodicalIF":0.0,"publicationDate":"2025-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145529208","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-05DOI: 10.1016/j.dim.2025.100107
Huiru Chen , Zhenhua Wang , Ming Ren
As Large language models (LLMs) continue to advance, the autonomous agents built upon them—LLM-based Autonomous Agents (LLMAAs) —are becoming more capable and widely used. While existing research has primarily focused on the capabilities of individual AI agents or their collaboration with humans, less is known about the emergent behaviors that arise when LLMAAs interact with each other at scale. This study addresses this gap by examining the collective behavior of LLMAAs in Chirper, a social simulation platform exclusively inhabited by AI agents. Drawing on theories from social network analysis and machine behavior, we investigate whether LLMAAs exhibit social dynamics commonly found in human communities, such as clustering, influential hubs, and homophily. Our findings reveal that LLMAAs form structured interaction networks that share key properties with human social systems, including power-law degree distributions and interaction homophily, though without exhibiting typical small-world characteristics. These insights represent an early step toward understanding the collective behavior of autonomous AI agents. They contribute to the emerging field of AI sociality and help inform the design of future multi-agent systems for engineering and social science applications.
{"title":"Unveiling the collective behaviors of large language model-based autonomous agents in an online community: A social network analysis perspective","authors":"Huiru Chen , Zhenhua Wang , Ming Ren","doi":"10.1016/j.dim.2025.100107","DOIUrl":"10.1016/j.dim.2025.100107","url":null,"abstract":"<div><div>As Large language models (LLMs) continue to advance, the autonomous agents built upon them—LLM-based Autonomous Agents (LLMAAs) —are becoming more capable and widely used. While existing research has primarily focused on the capabilities of individual AI agents or their collaboration with humans, less is known about the emergent behaviors that arise when LLMAAs interact with each other at scale. This study addresses this gap by examining the collective behavior of LLMAAs in Chirper, a social simulation platform exclusively inhabited by AI agents. Drawing on theories from social network analysis and machine behavior, we investigate whether LLMAAs exhibit social dynamics commonly found in human communities, such as clustering, influential hubs, and homophily. Our findings reveal that LLMAAs form structured interaction networks that share key properties with human social systems, including power-law degree distributions and interaction homophily, though without exhibiting typical small-world characteristics. These insights represent an early step toward understanding the collective behavior of autonomous AI agents. They contribute to the emerging field of AI sociality and help inform the design of future multi-agent systems for engineering and social science applications.</div></div>","PeriodicalId":72769,"journal":{"name":"Data and information management","volume":"10 1","pages":"Article 100107"},"PeriodicalIF":0.0,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145529207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-05DOI: 10.1016/j.dim.2025.100109
Yuepeng Li, Ziming Zeng, Qingqing Li, Shouqiang Sun, Yu Liu
To address the insufficient attention to the technological value potential and diffusion ability of topics in current evolution analysis, this study employs patent data from 2014 to 2023 in the domains of speech and image recognition. A tripartite "keywords-topics-documents" network is constructed using the BERTopic model for evaluation analysis. The evolution patterns of technological value potential and diffusion ability are investigated through the analysis of keyword associations and patent literature related to technical topics. By examining the evolution trajectories of technical topics and integrating value potential and diffusion ability analyses—based on keyword weights calculated using TextRank and patent citation frequencies—this research reveals a trend of cross-fusion in speech and image recognition topics. This trend is characterized by the incorporation of deep learning and multimodal recognition technologies. The value potential of technological topics exhibits an initial decline followed by a subsequent rise, while the diffusion ability demonstrates a continuous downward trend. This study provides intellectual support for technological forecasting and patent analytics.
{"title":"Evolution analysis of technological topics value potential and diffusion ability based on a three-tier network","authors":"Yuepeng Li, Ziming Zeng, Qingqing Li, Shouqiang Sun, Yu Liu","doi":"10.1016/j.dim.2025.100109","DOIUrl":"10.1016/j.dim.2025.100109","url":null,"abstract":"<div><div>To address the insufficient attention to the technological value potential and diffusion ability of topics in current evolution analysis, this study employs patent data from 2014 to 2023 in the domains of speech and image recognition. A tripartite \"keywords-topics-documents\" network is constructed using the BERTopic model for evaluation analysis. The evolution patterns of technological value potential and diffusion ability are investigated through the analysis of keyword associations and patent literature related to technical topics. By examining the evolution trajectories of technical topics and integrating value potential and diffusion ability analyses—based on keyword weights calculated using TextRank and patent citation frequencies—this research reveals a trend of cross-fusion in speech and image recognition topics. This trend is characterized by the incorporation of deep learning and multimodal recognition technologies. The value potential of technological topics exhibits an initial decline followed by a subsequent rise, while the diffusion ability demonstrates a continuous downward trend. This study provides intellectual support for technological forecasting and patent analytics.</div></div>","PeriodicalId":72769,"journal":{"name":"Data and information management","volume":"10 1","pages":"Article 100109"},"PeriodicalIF":0.0,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145529206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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