Online Social Networks and Media最新文献_第4页

Modeling and simulation of interventions’ effect on the spread of toxicity in social media 建模和模拟干预措施对社交媒体中毒性传播的影响

Online Social Networks and Media Pub Date : 2025-05-01 Epub 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":"2025-05-01","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

Explaining homophily without social selection: The role of transitivity in the formation of homophilic ties 解释没有社会选择的同质性：及物性在同质关系形成中的作用

Online Social Networks and Media Pub Date : 2025-05-01 Epub 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":"2025-05-01","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

Advancing depression detection on social media platforms through fine-tuned large language models 通过微调的大型语言模型，在社交媒体平台上推进抑郁症检测

Online Social Networks and Media Pub Date : 2025-05-01 Epub 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":"2025-05-01","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

DisTGranD: Granular event/sub-event classification for disaster response DisTGranD：灾难响应的细粒度事件/子事件分类

Online Social Networks and Media Pub Date : 2025-01-01 Epub Date: 2024-12-27 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

HaRNaT - A dynamic hashtag recommendation system using news HaRNaT - 利用新闻的动态标签推荐系统

Online Social Networks and Media Pub Date : 2025-01-01 Epub Date: 2024-11-23 DOI: 10.1016/j.osnem.2024.100294

Divya Gupta, Shampa Chakraverty

{"title":"HaRNaT - A dynamic hashtag recommendation system using news","authors":"Divya Gupta, Shampa Chakraverty","doi":"10.1016/j.osnem.2024.100294","DOIUrl":"10.1016/j.osnem.2024.100294","url":null,"abstract":"<div><div>Microblogging platforms such as <em>X</em> and <em>Mastadon</em> have evolved into significant data sources, where the Hashtag Recommendation System (HRS) is being devised to automate the recommendation of hashtags for user queries. We propose a context-sensitive, Machine Learning based HRS named <em>HaRNaT</em>, that strategically leverages news articles to identify pertinent keywords and subjects related to a query. It interprets the fresh context of a query and tracks the evolving dynamics of hashtags to evaluate their relevance in the present context. In contrast to prior methods that primarily rely on microblog content for hashtag recommendation, <em>HaRNaT</em> mines contextually related microblogs and assesses the relevance of co-occurring hashtags with news information. To accomplish this, it evaluates hashtag features, including pertinence, popularity among users, and association with other hashtags. In performance evaluation of <em>HaRNaT</em> trained on these features demonstrates a macro-averaged precision of 84% with Naive Bayes and 80% with Logistic Regression. Compared to <em>Hashtagify</em>- a hashtag search engine, <em>HaRNaT</em> offers a dynamically evolving set of hashtags.</div></div>","PeriodicalId":52228,"journal":{"name":"Online Social Networks and Media","volume":"45 ","pages":"Article 100294"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142702795","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 影响者在Instagram上的自我披露实践：一项多国纵向研究

Online Social Networks and Media Pub Date : 2025-01-01 Epub Date: 2024-12-21 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

BD2TSumm: A Benchmark Dataset for Abstractive Disaster Tweet Summarization bd2tsum：用于抽象灾难推文摘要的基准数据集

Online Social Networks and Media Pub Date : 2025-01-01 Epub Date: 2025-01-10 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

Harnessing prompt-based large language models for disaster monitoring and automated reporting from social media feedback 利用基于提示的大型语言模型，从社交媒体反馈中进行灾害监测和自动报告

Online Social Networks and Media Pub Date : 2025-01-01 Epub Date: 2024-11-25 DOI: 10.1016/j.osnem.2024.100295

Riccardo Cantini, Cristian Cosentino, Fabrizio Marozzo, Domenico Talia, Paolo Trunfio

{"title":"Harnessing prompt-based large language models for disaster monitoring and automated reporting from social media feedback","authors":"Riccardo Cantini, Cristian Cosentino, Fabrizio Marozzo, Domenico Talia, Paolo Trunfio","doi":"10.1016/j.osnem.2024.100295","DOIUrl":"10.1016/j.osnem.2024.100295","url":null,"abstract":"<div><div>In recent years, social media has emerged as one of the main platforms for real-time reporting of issues during disasters and catastrophic events. While great strides have been made in collecting such information, there remains an urgent need to improve user reports’ automation, aggregation, and organization to streamline various tasks, including rescue operations, resource allocation, and communication with the press. This paper introduces an innovative methodology that leverages the power of prompt-based Large Language Models (LLMs) to strengthen disaster response and management. By analyzing large volumes of user-generated content, our methodology identifies issues reported by citizens who have experienced a disastrous event, such as damaged buildings, broken gas pipelines, and flooding. It also localizes all posts containing references to geographic information in the text, allowing for aggregation of posts that occurred nearby. By leveraging these localized citizen-reported issues, the methodology generates insightful reports full of essential information for emergency services, news agencies, and other interested parties. Extensive experimentation on large datasets validates the accuracy and efficiency of our methodology in classifying posts, detecting sub-events, and producing real-time reports. These findings highlight the practical value of prompt-based LLMs in disaster response, emphasizing their flexibility and adaptability in delivering timely insights that support more effective interventions.</div></div>","PeriodicalId":52228,"journal":{"name":"Online Social Networks and Media","volume":"45 ","pages":"Article 100295"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142702794","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 政治符号如何在在线社交网络中传播：使用基于主体的模型来复制Twitter中黄丝带的复杂传染

Online Social Networks and Media Pub Date : 2025-01-01 Epub Date: 2025-02-04 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

Why are you traveling? Inferring trip profiles from online reviews and domain-knowledge 你为什么要旅行？从在线评论和领域知识推断旅行概况

Online Social Networks and Media Pub Date : 2025-01-01 Epub Date: 2025-02-01 DOI: 10.1016/j.osnem.2024.100296

Lucas G.S. Félix, Washington Cunha, Claudio M.V. de Andrade, Marcos André Gonçalves, Jussara M. Almeida

{"title":"Why are you traveling? Inferring trip profiles from online reviews and domain-knowledge","authors":"Lucas G.S. Félix, Washington Cunha, Claudio M.V. de Andrade, Marcos André Gonçalves, Jussara M. Almeida","doi":"10.1016/j.osnem.2024.100296","DOIUrl":"10.1016/j.osnem.2024.100296","url":null,"abstract":"<div><div>This paper addresses the task of inferring trip profiles (TPs), which consists of determining the profile of travelers engaged in a particular trip given a set of possible categories. TPs may include working trips, leisure journeys with friends, or family vacations. Travelers with different TPs typically have varied plans regarding destinations and timing. TP inference may provide significant insights for numerous tourism-related services, such as geo-recommender systems and tour planning. We focus on TP inference using TripAdvisor, a prominent tourism-centric social media platform, as our data source. Our goal is to evaluate how effectively we can automatically discern the TP from a user review on this platform. A user review encompasses both textual feedback and domain-specific data (such as a user’s previous visits to the location), which are crucial for accurately characterizing the trip. To achieve this, we assess various feature sets (including text and domain-specific) and implement advanced machine learning models, such as neural Transformers and open-source Large Language Models (Llama 2, Bloom). We examine two variants of the TP inference task—binary and multi-class. Surprisingly, our findings reveal that combining domain-specific features with TF-IDF-based representation in an LGBM model performs as well as more complex Transformer and LLM models, while being much more efficient and interpretable.</div></div>","PeriodicalId":52228,"journal":{"name":"Online Social Networks and Media","volume":"45 ","pages":"Article 100296"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143095300","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