Online Social Networks and Media最新文献

Advancing depression detection on social media platforms through fine-tuned large language models

Online Social Networks and Media Pub Date : 2025-03-21 DOI: 10.1016/j.osnem.2025.100311

Shahid Munir Shah , Syeda Anshrah Gillani , Mirza Samad Ahmed Baig , Muhammad Aamer Saleem , Muhammad Hamzah Siddiqui

{"title":"Advancing depression detection on social media platforms through fine-tuned large language models","authors":"Shahid Munir Shah , Syeda Anshrah Gillani , Mirza Samad Ahmed Baig , Muhammad Aamer Saleem , Muhammad Hamzah Siddiqui","doi":"10.1016/j.osnem.2025.100311","DOIUrl":"10.1016/j.osnem.2025.100311","url":null,"abstract":"<div><div>This study investigates the use of Large Language Models (LLMs) for improved depression detection from users’ social media data. Through the use of fine-tuned GPT-3.5 Turbo 1106 and LLaMA2-7B models and a sizable dataset from earlier studies, we were able to identify depressed content in social media posts with a high accuracy of 96.4%. The comparative analysis of the obtained results with the relevant studies in the literature and the base models shows that the proposed fine-tuned LLMs achieved enhanced performance compared to existing state-of-the-art systems and the base models. This demonstrates the robustness of LLM-based fine-tuned systems to be used as potential depression detection systems. The study describes the approach in depth, including the parameters used and the fine-tuning procedure, along with the dataset description and comparative summary of the results, indicating the important implications of the obtained results for the early diagnosis of depression on various social media platforms.</div></div>","PeriodicalId":52228,"journal":{"name":"Online Social Networks and Media","volume":"46 ","pages":"Article 100311"},"PeriodicalIF":0.0,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143683376","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}

引用次数: 0

Community detection in Multimedia Social Networks using an attributed graph model

Online Social Networks and Media Pub Date : 2025-03-20 DOI: 10.1016/j.osnem.2025.100312

Giancarlo Sperlì

{"title":"Community detection in Multimedia Social Networks using an attributed graph model","authors":"Giancarlo Sperlì","doi":"10.1016/j.osnem.2025.100312","DOIUrl":"10.1016/j.osnem.2025.100312","url":null,"abstract":"<div><div>In this paper, we design a novel data model for a Multimedia Social Network, that has been modeled as an attribute graph for integrating semantic analysis of multimedia content published by users. It combines features inferred from object detection, image classification, and hashtag analysis in a unified model to characterize a user from different points of view. On top of this model, community detection algorithms have been applied to unveil users’ communities. Hence, we design a framework integrating multimedia features with different community detection approaches (topological, deep learning, representation learning, and game theory-based) to improve detection effectiveness. The proposed framework has been evaluated on a real-world dataset, composed of 4.5 million profiles publishing more than 42 million posts and 1.2 million images, to investigate the impact of different features on both graph-building and community detection tasks. The main findings of the proposed analysis show how combining different sets of features inferred from multimedia content allows to achieve the highest modularity score w.r.t. other configurations although it requires a higher running time for building the underlined network. Specifically, representation and game theory-based algorithms achieve the highest results in terms of Modularity measure by exploiting the semantic and contextual information integrated into the proposed model.</div></div>","PeriodicalId":52228,"journal":{"name":"Online Social Networks and Media","volume":"46 ","pages":"Article 100312"},"PeriodicalIF":0.0,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143683310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Explaining homophily without social selection: The role of transitivity in the formation of homophilic ties

Online Social Networks and Media Pub Date : 2025-03-17 DOI: 10.1016/j.osnem.2025.100310

Alexander V. Gubanov , Vyacheslav L. Goiko , Ivan V. Kozitsin

{"title":"Explaining homophily without social selection: The role of transitivity in the formation of homophilic ties","authors":"Alexander V. Gubanov , Vyacheslav L. Goiko , Ivan V. Kozitsin","doi":"10.1016/j.osnem.2025.100310","DOIUrl":"10.1016/j.osnem.2025.100310","url":null,"abstract":"<div><div>This paper investigates if an assortative social network may further amplify its assortativity following two distinct interventions: linking a random unconnected pair in the neighborhood of a vertex chosen by chance (N-protocol) or connecting a randomly selected open triplet (T-protocol). Under a series of assumptions, we derive a closed-form expression that links the expected change in assortativity caused by N-protocol and the current assortativity score. For the binary nodal characteristic or some other settings, this expression turns out to be a simple quadratic dependency, which indicates that networks with assortative mixing should become less assortative following N-protocol. Next, we provide a sufficient condition that N- and T-protocols will cause the same assortativity change. Using numerical experiments with synthetic and empirical networks with various assortativity rates, we demonstrate that our theoretical estimations tend to be quite accurate for topologies with moderate assortativity. However, for topologies with relatively high assortativity levels, the factual values of the assortativity shift tend to be greater than theoretical ones, but still negative. Empirical topologies are also characterized by relatively large divergences between N- and T-protocols, with the latter typically providing a higher assortativity decrease. We carefully explain these findings by analyzing local homophily patterns. We also consider a modified version of T-protocol that connects individuals having no less than a predefined number of common peers. For a corpus of empirical networks, we managed to characterize the threshold values of assortativity and the number of common friends above which the assortativity rate will increase following this intervention.</div></div>","PeriodicalId":52228,"journal":{"name":"Online Social Networks and Media","volume":"46 ","pages":"Article 100310"},"PeriodicalIF":0.0,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143632120","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}

引用次数: 0

Modeling and simulation of interventions’ effect on the spread of toxicity in social media

Online Social Networks and Media Pub Date : 2025-03-10 DOI: 10.1016/j.osnem.2025.100309

Emmanuel Addai , Nitin Agarwal , Niloofar Yousefi

{"title":"Modeling and simulation of interventions’ effect on the spread of toxicity in social media","authors":"Emmanuel Addai , Nitin Agarwal , Niloofar Yousefi","doi":"10.1016/j.osnem.2025.100309","DOIUrl":"10.1016/j.osnem.2025.100309","url":null,"abstract":"<div><div>The prevalence of toxicity on social media platforms constitutes a significant issue. Gaining insights into the factors that contribute to toxicity is essential for devising effective strategies to mitigate it. In this work, we extend and evaluate fractional control SEIQR (Susceptible, Exposed, Infected, Quarantined, Recovered) epidemiological modeling incorporating government interventions and awareness programs. The model incorporates different infected groups, moderate and high infected users, and is used to investigate the influence by each user on the overall spread of toxicity. We have evaluated the toxic post-free equilibrium point, the reproduction number <span><math><mrow><mo>(</mo><msub><mrow><mi>R</mi></mrow><mrow><mn>0</mn></mrow></msub><mo>)</mo></mrow></math></span>, the existence-uniqueness, and the stability point. We performed the model sensitivity analysis using the Latin Hypercube Sampling-Partial Rank Correlation Coefficient (LHS-PRCC) method. For data fitting analysis, we examined data from COVID-19-related tweets. We examine the intricacies of the proposed numerical scheme, providing a robust framework for analyzing and comprehending online toxicity spread. Simulations were conducted to elucidate the effects of government interventions and public awareness programs on the prevalence and dynamics of online toxicity spread. The study’s primary accomplishment is the model’s reduction of the error rate to 0.0011. This is distinguished by the reduced need to remove users from the network. The model not only improves accuracy but also maintains a larger user base, indicating an efficient, user-centric strategy. The results suggest that both awareness programs and government interventions are crucial for managing and mitigating online toxicity spread. This study will significantly assist network providers and policymakers to identify the infected users, thereby reducing toxic conversations.</div></div>","PeriodicalId":52228,"journal":{"name":"Online Social Networks and Media","volume":"46 ","pages":"Article 100309"},"PeriodicalIF":0.0,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143579436","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}

引用次数: 0

An explainable ensemble model for revealing the level of depression in social media by considering personality traits and sentiment polarity pattern

Online Social Networks and Media Pub Date : 2025-03-08 DOI: 10.1016/j.osnem.2025.100307

Gede Aditra Pradnyana , Wiwik Anggraeni , Eko Mulyanto Yuniarno , Mauridhi Hery Purnomo

{"title":"An explainable ensemble model for revealing the level of depression in social media by considering personality traits and sentiment polarity pattern","authors":"Gede Aditra Pradnyana , Wiwik Anggraeni , Eko Mulyanto Yuniarno , Mauridhi Hery Purnomo","doi":"10.1016/j.osnem.2025.100307","DOIUrl":"10.1016/j.osnem.2025.100307","url":null,"abstract":"<div><div>Early detection of depression in mental health is crucial for better intervention. Social media has been extensively used to examine users’ behavior, motivating researchers to develop an automatic depression detection model. However, the accuracy and clarity of the reasons behind the detection results still need to be improved. Current research focuses primarily on syntactic and semantic information in user-posted texts, while other aspects of users’ psychological characteristics are often overlooked. Therefore, this study addresses the gap by proposing a novel model integrating personality traits and sentiment polarity patterns into an explainable ensemble model. Specifically, we developed two base learners for the averaged and meta-ensemble learning strategy. The first learner employed the Robustly Optimized BERT Pre-training Approach (RoBERTa). For the second learner, we combined the Random Forest and Bidirectional Long Short-Term Memory (RF-BiLSTM) methods to effectively handle the combination of personality traits and sequential information in sentiment polarity patterns. These additional features are obtained by performing domain adaptation for personality prediction and sentiment analysis using a lexicon-based model. Based on the experimental results, our ensemble model improved depression detection results by leveraging the strengths of each base learner. Our model advanced the state-of-the-art, outperforming existing models with an increase in accuracy and F1-score of 4.14% and 2.99%, respectively. The model successfully enhanced the interpretability of detection results, providing a more comprehensive understanding of the factors underlying depressive symptoms. This research highlights the potential of considering alternative additional features as a promising avenue for enhancing depression detection in social media.</div></div>","PeriodicalId":52228,"journal":{"name":"Online Social Networks and Media","volume":"46 ","pages":"Article 100307"},"PeriodicalIF":0.0,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143579435","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}

引用次数: 0

Management of psychological emergency cases on social media: A hybrid approach combining knowledge graphs and graph neural networks

Online Social Networks and Media Pub Date : 2025-03-05 DOI: 10.1016/j.osnem.2025.100308

Mourad Ellouze , Sonda Rekik , Lamia Hadrich Belguith

{"title":"Management of psychological emergency cases on social media: A hybrid approach combining knowledge graphs and graph neural networks","authors":"Mourad Ellouze , Sonda Rekik , Lamia Hadrich Belguith","doi":"10.1016/j.osnem.2025.100308","DOIUrl":"10.1016/j.osnem.2025.100308","url":null,"abstract":"<div><div>The effects of psychological crises are evolving at an astounding rate nowadays, presenting a significant challenge for everyone involved in tracking these disorders. Therefore, we propose in this paper a hybrid approach based on linguistic processing and numerical techniques allowing to: (i) identify the presence of psychological emergencies among social network users by analyzing their textual production, (ii) determine the specific type of emergency case, (iii) elaborate a graph for each type of emergency, reflecting the different dimensions linked to the psychological emergency, allowing for a better diagnosis of the situation and providing an overall view of the crisis type, (iv) combine the separate graphs for each emergency to address the various semantic aspects. The work was accomplished using advanced language model techniques, knowledge graphs and neural network graphs. The combination of these techniques ensures that their advantages are leveraged while overcoming their limitations in terms of result generalization. The evaluation of different parts related to detecting the presence of psychological problems, predicting specific type of emergency cases, and detecting links between knowledge graphs was measured using the F-measure metric. The values derived from this measure, corresponding to the evaluation of these three tasks, are, respectively, 83%, 87% and 80%. For the evaluation of the elaboration of each graph related to specific type of emergency cases, this was accomplished using qualitative metric standards. The results obtained can be considered encouraging given the significant scale of our approach.</div></div>","PeriodicalId":52228,"journal":{"name":"Online Social Networks and Media","volume":"46 ","pages":"Article 100308"},"PeriodicalIF":0.0,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143551494","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}

引用次数: 0

DisTGranD: Granular event/sub-event classification for disaster response

Online Social Networks and Media Pub Date : 2025-01-01 DOI: 10.1016/j.osnem.2024.100297

Ademola Adesokan , Sanjay Madria , Long Nguyen

{"title":"DisTGranD: Granular event/sub-event classification for disaster response","authors":"Ademola Adesokan , Sanjay Madria , Long Nguyen","doi":"10.1016/j.osnem.2024.100297","DOIUrl":"10.1016/j.osnem.2024.100297","url":null,"abstract":"<div><div>Efficient crisis management relies on prompt and precise analysis of disaster data from various sources, including social media. The advantage of fine-grained, annotated, class-labeled data is the provision of a diversified range of information compared to high-level label datasets. In this study, we introduce a dataset richly annotated at a low level to more accurately classify crisis-related communication. To this end, we first present DisTGranD, an extensively annotated dataset of over 47,600 tweets related to earthquakes and hurricanes. The dataset uses the Automatic Content Extraction (ACE) standard to provide detailed classification into dual-layer annotation for events and sub-events and identify critical triggers and supporting arguments. The inter-annotator evaluation of DisTGranD demonstrated high agreement among annotators, with Fleiss Kappa scores of 0.90 and 0.93 for event and sub-event types, respectively. Moreover, a transformer-based embedded phrase extraction method showed XLNet achieving an impressive 96% intra-label similarity score for event type and 97% for sub-event type. We further proposed a novel deep learning classification model, RoBiCCus, which achieved <span><math><mrow><mo>≥</mo><mn>90</mn><mtext>%</mtext></mrow></math></span> accuracy and F1-Score in the event and sub-event type classification tasks on our DisTGranD dataset and outperformed other models on publicly available disaster datasets. DisTGranD dataset represents a nuanced class-labeled framework for detecting and classifying disaster-related social media content, which can significantly aid decision-making in disaster response. This robust dataset enables deep-learning models to provide insightful, actionable data during crises. Our annotated dataset and code are publicly available on GitHub <span><span><sup>1</sup></span></span>.</div></div>","PeriodicalId":52228,"journal":{"name":"Online Social Networks and Media","volume":"45 ","pages":"Article 100297"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143095218","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}

引用次数: 0

BD2TSumm: A Benchmark Dataset for Abstractive Disaster Tweet Summarization

Online Social Networks and Media Pub Date : 2025-01-01 DOI: 10.1016/j.osnem.2024.100299

Piyush Kumar Garg , Roshni Chakraborty , Sourav Kumar Dandapat

{"title":"BD2TSumm: A Benchmark Dataset for Abstractive Disaster Tweet Summarization","authors":"Piyush Kumar Garg , Roshni Chakraborty , Sourav Kumar Dandapat","doi":"10.1016/j.osnem.2024.100299","DOIUrl":"10.1016/j.osnem.2024.100299","url":null,"abstract":"<div><div>Online social media platforms, such as Twitter, are mediums for valuable updates during disasters. However, the large scale of available information makes it difficult for humans to identify relevant information from the available information. An automatic summary of these tweets provides identification of relevant information easy and ensures a holistic overview of a disaster event to process the aid for disaster response. In literature, there are two types of abstractive disaster tweet summarization approaches based on the format of output summary: key-phrased-based (where summary is a set of key-phrases) and sentence-based (where summary is a paragraph consisting of sentences). Existing sentence-based abstractive approaches are either unsupervised or supervised. However, both types of approaches require a sizable amount of ground-truth summaries for training and/or evaluation such that they work on disaster events irrespective of type and location. The lack of abstractive disaster ground-truth summaries and guidelines for annotation motivates us to come up with a systematic procedure to create abstractive sentence ground-truth summaries of disaster events. Therefore, this paper presents a two-step systematic annotation procedure for sentence-based abstractive summary creation. Additionally, we release <em>BD2TSumm</em>, i.e., a benchmark ground-truth dataset for evaluating the sentence-based abstractive summarization approaches for disaster events. <em>BD2TSumm</em> consists of 15 ground-truth summaries belonging to 5 different continents and both natural and man-made disaster types. Furthermore, to ensure the high quality of the generated ground-truth summaries, we evaluate them qualitatively (using five metrics) and quantitatively (using two metrics). Finally, we compare 12 existing State-Of-The-Art (SOTA) abstractive summarization approaches on these ground-truth summaries using ROUGE-N F1-score.</div></div>","PeriodicalId":52228,"journal":{"name":"Online Social Networks and Media","volume":"45 ","pages":"Article 100299"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143095219","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}

引用次数: 0

Influencer self-disclosure practices on Instagram: A multi-country longitudinal study

Online Social Networks and Media Pub Date : 2025-01-01 DOI: 10.1016/j.osnem.2024.100298

Thales Bertaglia , Catalina Goanta , Gerasimos Spanakis , Adriana Iamnitchi

{"title":"Influencer self-disclosure practices on Instagram: A multi-country longitudinal study","authors":"Thales Bertaglia , Catalina Goanta , Gerasimos Spanakis , Adriana Iamnitchi","doi":"10.1016/j.osnem.2024.100298","DOIUrl":"10.1016/j.osnem.2024.100298","url":null,"abstract":"<div><div>This paper presents a longitudinal study of more than ten years of activity on Instagram consisting of over a million posts by 400 content creators from four countries: the US, Brazil, Netherlands and Germany. Our study shows differences in the professionalisation of content monetisation between countries, yet consistent patterns; significant differences in the frequency of posts yet similar user engagement trends; and significant differences in the disclosure of sponsored content in some countries, with a direct connection with national legislation. We analyse shifts in marketing strategies due to legislative and platform feature changes, focusing on how content creators adapt disclosure methods to different legal environments. We also analyse the impact of disclosures and sponsored posts on engagement and conclude that, although sponsored posts have lower engagement on average, properly disclosing ads does not reduce engagement further. Our observations stress the importance of disclosure compliance and can guide authorities in developing and monitoring them more effectively.</div></div>","PeriodicalId":52228,"journal":{"name":"Online Social Networks and Media","volume":"45 ","pages":"Article 100298"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143095211","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

How political symbols spread in online social networks: Using agent-based models to replicate the complex contagion of the yellow ribbon in Twitter

Online Social Networks and Media Pub Date : 2025-01-01 DOI: 10.1016/j.osnem.2025.100300

Francisco J. León-Medina

{"title":"How political symbols spread in online social networks: Using agent-based models to replicate the complex contagion of the yellow ribbon in Twitter","authors":"Francisco J. León-Medina","doi":"10.1016/j.osnem.2025.100300","DOIUrl":"10.1016/j.osnem.2025.100300","url":null,"abstract":"<div><div>This paper analyzes the diffusion of the yellow ribbon in Twitter, a political symbol that represents the demand for the release of Catalan prisoners. We gathered data on potential users of the symbol in Twitter (users that publicly backed the cause), including their social network of friendships, and built an agent-based simulation to replicate the diffusion of the symbol in a digital twin version of the observed network. Our hypothesis was that complex contagion is the best explanation of the observed statistical relation between the proportion of adopting neighbors and the probability of adoption. Results show that the complex contagion model outperforms the simple contagion model and generates a better fit between the observed and the simulated pattern when the typical conditions of a complex contagion process are added to the baseline model, that is, when agents are affected by their reference group behavior rather than by the most influential nodes of the network, and when we identify a peripherical and densely connected network community and trigger the process from there. These results widen the set of behaviors whose diffusion can be explained as complex contagion to include adoption in low-risk/low-cost behaviors among people who would usually <em>not</em> resist adoption.</div></div>","PeriodicalId":52228,"journal":{"name":"Online Social Networks and Media","volume":"45 ","pages":"Article 100300"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143095301","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}

引用次数: 0