2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)最新文献_第9页

A Knowledge-driven Domain Adaptive Approach to Early Misinformation Detection in an Emergent Health Domain on Social Media 基于社交媒体的紧急健康领域早期错误信息检测的知识驱动领域自适应方法

2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2022-11-10 DOI: 10.1109/ASONAM55673.2022.10068587

Lanyu Shang, Yang Zhang, Zhenrui Yue, YeonJung Choi, Huimin Zeng, Dong Wang

{"title":"A Knowledge-driven Domain Adaptive Approach to Early Misinformation Detection in an Emergent Health Domain on Social Media","authors":"Lanyu Shang, Yang Zhang, Zhenrui Yue, YeonJung Choi, Huimin Zeng, Dong Wang","doi":"10.1109/ASONAM55673.2022.10068587","DOIUrl":"https://doi.org/10.1109/ASONAM55673.2022.10068587","url":null,"abstract":"This paper focuses on an important problem of early misinformation detection in an emergent health domain on social media. Current misinformation detection solutions often suffer from the lack of resources (e.g., labeled datasets, sufficient medical knowledge) in the emerging health domain to accurately identify online misinformation at an early stage. To address such a limitation, we develop a knowledge-driven domain adaptive approach that explores a good set of annotated data and reliable knowledge facts in a source domain (e.g., COVID-19) to learn the domain-invariant features that can be adapted to detect misinformation in the emergent target domain with little ground truth labels (e.g., Monkeypox). Two critical challenges exist in developing our solution: i) how to leverage the noisy knowledge facts in the source domain to obtain the medical knowledge related to the target domain? ii) How to adapt the domain discrepancy between the source and target domains to accurately assess the truthfulness of the social media posts in the target domain? To address the above challenges, we develop KAdapt, a knowledge-driven domain adaptive early misinformation detection framework that explicitly extracts rel-evant knowledge facts from the source domain and jointly learns the domain-invariant representation of the social media posts and their relevant knowledge facts to accurately identify misleading posts in the target domain. Evaluation results on five real-world datasets demonstrate that KAdapt significantly outperforms state-of-the-art baselines in terms of accurately detecting misleading Monkeypox posts on social media.","PeriodicalId":423113,"journal":{"name":"2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129510058","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: 1

PIMan: A Comprehensive Approach for Establishing Plausible Influence among Software Repositories 在软件存储库之间建立合理影响的综合方法

2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2022-11-10 DOI: 10.1109/ASONAM55673.2022.10068629

Md Omar Faruk Rokon, Risul Islam, Md Rayhanul Masud, M. Faloutsos

{"title":"PIMan: A Comprehensive Approach for Establishing Plausible Influence among Software Repositories","authors":"Md Omar Faruk Rokon, Risul Islam, Md Rayhanul Masud, M. Faloutsos","doi":"10.1109/ASONAM55673.2022.10068629","DOIUrl":"https://doi.org/10.1109/ASONAM55673.2022.10068629","url":null,"abstract":"How can we quantify the influence among repos-itories in online archives like GitHub? Determining repository influence is an essential building block for understanding the dynamics of GitHub-like software archives. The key challenge is to define the appropriate representation model of influence that captures the nuances of the concept and considers its diverse manifestations. We propose PIMan, a systematic approach to quantify the influence among the repositories in a software archive by focusing on the social level interactions. As our key novelty, we introduce the concept of Plausible Influence which considers three types of information: (a) repository level interactions, (b) author level interactions, and (c) temporal considerations. We evaluate and apply our method using 2089 malware repositories from GitHub spanning approximately 12 years. First, we show how our approach provides a powerful and flexible way to generate a plausible influence graph whose density is determined by the Plausible Influence Threshold (PIT), which is modifiable to meet the needs of a study. Second, we find that there is a significant collaboration and influence among the repositories in our dataset. We identify 28 connected components in the plausible influence graph (PIT = 0.25) with 7% of the components containing at least 15 repositories. Furthermore, we find 19 repositories that influenced at least 10 other repositories directly and spawned at least two “families” of repositories. In addition, the results show that our influence metrics capture the manifold aspects of the interactions that are not captured by the typical repository popularity metrics (e.g. number of stars). Overall, our work is a fundamental building block for identifying the influence and lineage of the repositories in online software platforms.","PeriodicalId":423113,"journal":{"name":"2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123980165","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

Session-based News Recommendation from Temporal User Commenting Dynamics 基于会话的用户评论动态新闻推荐

2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2022-11-10 DOI: 10.1109/ASONAM55673.2022.10068595

Chen Shen, Chao Han, Lihong He, Arjun Mukherjee, Z. Obradovic, E. Dragut

{"title":"Session-based News Recommendation from Temporal User Commenting Dynamics","authors":"Chen Shen, Chao Han, Lihong He, Arjun Mukherjee, Z. Obradovic, E. Dragut","doi":"10.1109/ASONAM55673.2022.10068595","DOIUrl":"https://doi.org/10.1109/ASONAM55673.2022.10068595","url":null,"abstract":"With the increase in volume of daily online news items, it is more and more difficult for readers to identify news articles relevant to their interests. Thus, effective recommendation systems are critical for an effective user news consumption experience. Existing news recommendation methods usually rely on the news click history to model user interest. However, there are other signals about user behaviors, such as user commenting activity, which have not been used before. We propose a recommendation algorithm that predicts articles a user may be interested in, given her historical sequential commenting behavior on news articles. We show that following this sequential user behavior the news recommendation problem falls into in the class of session-based recommendation. The techniques in this class seek to model users' sequential and temporal behaviors. While we seek to follow the general directions in this space, we face unique challenges specific to news in modeling temporal dynamics, e.g., users' interests shift over time, users comment irregularly on articles, and articles are perishable items with limited lifespans. We propose a recency-regularized neural attentive framework for session-based news recommendation. The proposed method is able to capture the temporal dynamics of both users and news articles, while maintaining interpretability. We design a lag-aware attention and a recency regularization to model the time effect of news articles and comments. We conduct extensive empirical studies on 3 real-world news datasets to demonstrate the effectiveness of our method.","PeriodicalId":423113,"journal":{"name":"2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127614679","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

Automated Detection of Sockpuppet Accounts in Wikipedia 维基百科中Sockpuppet帐户的自动检测

2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2022-11-10 DOI: 10.1109/ASONAM55673.2022.10068604

M. Sakib, Francesca Spezzano

引用次数: 1

Automated Skin Lesion Segmentation using VGG-UNet 基于VGG-UNet的自动皮肤病变分割

2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2022-11-10 DOI: 10.1109/ASONAM55673.2022.10068634

Anwar Jimi, Hind Abouche, Nabila Zrira, Ibtissam Benmiloud

引用次数: 0

Causal Analysis on the Anchor Store Effect in a Location-based Social Network 基于位置的社交网络中锚店效应的因果分析

2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2022-10-24 DOI: 10.1109/ASONAM55673.2022.10068687

Anish K. Vallapuram, Young D. Kwon, Lik-Hang Lee, Fengli Xu, Pan Hui

{"title":"Causal Analysis on the Anchor Store Effect in a Location-based Social Network","authors":"Anish K. Vallapuram, Young D. Kwon, Lik-Hang Lee, Fengli Xu, Pan Hui","doi":"10.1109/ASONAM55673.2022.10068687","DOIUrl":"https://doi.org/10.1109/ASONAM55673.2022.10068687","url":null,"abstract":"A particular phenomenon of interest in Retail Eco-nomics is the spillover effect of anchor stores (specific stores with a reputable brand) to non-anchor stores in terms of customer traffic. Prior works in this area rely on small and survey-based datasets that are often confidential or expensive to collect on a large scale. Also, very few works study the underlying causal mechanisms between factors that underpin the spillover effect. In this work, we analyze the causal relationship between anchor stores and customer traffic to non-anchor stores and employ a propensity score matching framework to investigate this effect more efficiently. First of all, to demonstrate the effect, we leverage open and mobile data from London Datastore and Location-Based Social Networks (LBSNs) such as Foursquare. We then perform a large-scale empirical analysis of customer visit patterns from anchor stores to non-anchor stores (e.g., non-chain restaurants) located in the Greater London area as a case study. By studying over 600 neighbourhoods in the Greater London area, we find that anchor stores cause a 14.2-26.5% increase in customer traffic for the non-anchor stores reinforcing the established economic theory Moreover, we evaluate the efficiency of our methodology by studying the confounder balance, dose difference and performance of the matching framework on synthetic data. Through this work, we point decision-makers in the retail industry to a more systematic approach to estimate the anchor store effect and pave the way for further research to discover more complex causal relationships underlying this effect with open data.","PeriodicalId":423113,"journal":{"name":"2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122881716","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

Noise Audits Improve Moral Foundation Classification 噪音审计改善道德基础分类

2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2022-10-13 DOI: 10.1109/ASONAM55673.2022.10068681

Negar Mokhberian, F. R. Hopp, Bahareh Harandizadeh, Fred Morstatter, Kristina Lerman

{"title":"Noise Audits Improve Moral Foundation Classification","authors":"Negar Mokhberian, F. R. Hopp, Bahareh Harandizadeh, Fred Morstatter, Kristina Lerman","doi":"10.1109/ASONAM55673.2022.10068681","DOIUrl":"https://doi.org/10.1109/ASONAM55673.2022.10068681","url":null,"abstract":"Morality plays an important role in culture, identity, and emotion. Recent advances in natural language processing have shown that it is possible to classify moral values expressed in text at scale. Morality classification relies on human annotators to label the moral expressions in text, which provides training data to achieve state-of-the-art performance. However, these annotations are inherently subjective and some of the instances are hard to classify, resulting in noisy annotations due to error or lack of agreement. The presence of noise in training data harms the classifier's ability to accurately recognize moral foundations from text. We propose two metrics to audit the noise of annotations. The first metric is entropy of instance labels, which is a proxy measure of annotator disagreement about how the instance should be labeled. The second metric is the silhouette coefficient of a label assigned by an annotator to an instance. This metric leverages the idea that instances with the same label should have similar latent representations, and deviations from collective judgments are indicative of errors. Our experiments on three widely used moral foundations datasets show that removing noisy annotations based on the proposed metrics improves classification performance.11Our code can be found at: https://github.com/negar-mokhberian/noise-audits.","PeriodicalId":423113,"journal":{"name":"2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116421225","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: 1

Improving Code Review with GitHub Issue Tracking 改进代码审查与GitHub问题跟踪

2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2022-10-10 DOI: 10.1109/ASONAM55673.2022.10068709

Abduljaleel Al-Rubaye, G. Sukthankar

引用次数: 0

Unsupervised Domain Adaptation for COVID-19 Information Service with Contrastive Adversarial Domain Mixup 具有对抗性领域混淆的COVID-19信息服务无监督领域自适应

2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2022-10-06 DOI: 10.1109/ASONAM55673.2022.10068580

Huimin Zeng, Zhenrui Yue, Ziyi Kou, Lanyu Shang, Yang Zhang, Dong Wang

{"title":"Unsupervised Domain Adaptation for COVID-19 Information Service with Contrastive Adversarial Domain Mixup","authors":"Huimin Zeng, Zhenrui Yue, Ziyi Kou, Lanyu Shang, Yang Zhang, Dong Wang","doi":"10.1109/ASONAM55673.2022.10068580","DOIUrl":"https://doi.org/10.1109/ASONAM55673.2022.10068580","url":null,"abstract":"In the real-world application of COVID-19 misinformation detection, a fundamental challenge is the lack of the labeled COVID data to enable supervised end-to-end training of the models, especially at the early stage of the pandemic. To address this challenge, we propose an unsupervised domain adaptation framework using contrastive learning and adversarial domain mixup to transfer the knowledge from an existing source data domain to the target COVID-19 data domain. In particular, to bridge the gap between the source domain and the target domain, our method reduces a radial basis function (RBF) based discrepancy between these two domains. Moreover, we leverage the power of domain adversarial examples to establish an intermediate domain mixup, where the latent representations of the input text from both domains could be mixed during the training process. Extensive experiments on multiple real-world datasets suggest that our method can effectively adapt misinformation detection systems to the unseen COVID-19 target domain with significant improvements compared to the state-of-the-art baselines.","PeriodicalId":423113,"journal":{"name":"2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"378 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117139665","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: 5

Quantifying How Hateful Communities Radicalize Online Users 量化仇恨社区如何使在线用户变得激进

2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2022-09-19 DOI: 10.1109/ASONAM55673.2022.10068644

Matheus Schmitz, K. Burghardt, Goran Muric

{"title":"Quantifying How Hateful Communities Radicalize Online Users","authors":"Matheus Schmitz, K. Burghardt, Goran Muric","doi":"10.1109/ASONAM55673.2022.10068644","DOIUrl":"https://doi.org/10.1109/ASONAM55673.2022.10068644","url":null,"abstract":"While online social media offers a way for ignored or stifled voices to be heard, it also allows users a platform to spread hateful speech. Such speech usually originates in fringe communities, yet it can spill over into mainstream channels. In this paper, we measure the impact of joining fringe hateful communities in terms of hate speech propagated to the rest of the social network. We leverage data from Reddit to assess the effect of joining one type of echo chamber: a digital community of like-minded users exhibiting hateful behavior. We measure members' usage of hate speech outside the studied community before and after they become active participants. Using Interrupted Time Series (ITS) analysis as a causal inference method, we gauge the spillover effect, in which hateful language from within a certain community can spread outside that community by using the level of out-of-community hate word usage as a proxy for learned hate. We investigate four different Reddit sub-communities (subreddits) covering three areas of hate speech: racism, misogyny and fat-shaming. In all three cases we find an increase in hate speech outside the originating community, implying that joining such community leads to a spread of hate speech throughout the platform. Moreover, users are found to pick up this new hateful speech for months after initially joining the community. We show that the harmful speech does not remain contained within the community. Our results provide new evidence of the harmful effects of echo chambers and the potential benefit of moderating them to reduce adoption of hateful speech.","PeriodicalId":423113,"journal":{"name":"2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"379 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123448433","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: 7