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

{"title":"Characterizing Low Credibility Websites in Brazil through Computer Networking Attributes","authors":"João M. M. Couto, Julio C. S. Reis, Ítalo Cunha, Leandro Araújo, Fabrício Benevenuto","doi":"10.1109/ASONAM55673.2022.10068660","DOIUrl":"https://doi.org/10.1109/ASONAM55673.2022.10068660","url":null,"abstract":"A key gear in most misinformation ecosystems is the deployment of fake news web sites that publish news in a similar fashion to how news articles are put out by credible sources. The content offered by these sites is disseminated in a complex process that may involve automation, exploitation of message apps and social network algorithms, political bias, and targeted ads to reach large and niche audiences. Due to this high complexity and the rapidly evolving nature of the problem, we are just beginning to understand patterns in the various misinformation ecosystems on the Web. In this work, we offer a first step towards understanding network properties, including data from DNS records, domain registration, TLS certificates, and hosting infrastructure of Brazilian web sites associated with the dissemination of misinformation content on digital platforms. Our findings, in addition to providing a better understanding of the misinformation ecosystem in Brazil, also reveal a novel set of features useful to distinguish low credibility web sites from others.","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":"130216772","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}

{"title":"Maximizing Bigdata Retrieval: Block as a Value for NoSQL over SQL","authors":"A. Gidado, C. Ezeife","doi":"10.1109/ASONAM55673.2022.10068692","DOIUrl":"https://doi.org/10.1109/ASONAM55673.2022.10068692","url":null,"abstract":"This paper presents NoSQL Over SQL Block as a Value Database (NOSD), a system that speeds up data retrieval time and availability in very large relational databases. NOSD proposes a Block as a Value model (BaaV). Unlike a relational database model where a relation is $R(K, A_{1}, A_{2}, ldots A_{n})$, with a key attribute $K$ and a set of attributes of the relation: $A_{1}, A_{2}, ldots A_{n}$, BaaV represents a relation $R(K, r_{1}, r_{2}, ldots r_{n})$ with a key attribute $K$ and a set of $n$ relations called blocks. Each $r$ contains a set of its own attributes denoted as $r(k, a_{1}, a_{2},ldots a_{n})$ with a key attribute $k$ and a set of $n$ attributes. The relations $r_{1}, r_{2}, ldots r_{n}$ in $R$ are related through foreign key relationships to a super relation $R$ with primary key $K$. The BaaV model is then denoted in a keyed block format $R{K, B}$, where $K$ is a key to a block of values $B$ of partial relations implemented on NoSQL databases and replicating existing large relational database systems. As opposed to conventional systems such as Zidian, Google's Spanner, SparkSQL and Simple Buttom-Up (SBU) which implement SQL over NoSQL and replicate data into different nodes, NOSD implements NoSQL over SQL and uses Lucene functionality on NoSQL to enhance data retrieval costs. Experimenting with our proposed model, we demonstrated the performance of NOSD under the following conditions to prove its novelty (a) scan free queries, and (b) bounded queries on NoSQL databases. We showed that NOSD (a) performs excellently than ordinary relational databases (b) guarantees no scans for no scan queries (c) allows parallelization in query execution, and (d) can be deployed into existing SQL databases with guaranteed horizontal scalability, data retention and accurate autonomous data replication. Using existing benchmark systems, we demonstrated that NOSD outperforms existing SQL databases, SQL over NoSQL systems and is novel in ensuring that existing large SQL database systems utilize the functionalities of NoSQL databases without data loss. $A_{1}, A_{2}, ldots A_{n}$","PeriodicalId":423113,"journal":{"name":"2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"84 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":"130381918","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}

{"title":"Predicting Targeted Violence from Social Media Communication","authors":"Lisa Kaati, A. Shrestha, N. Akrami","doi":"10.1109/ASONAM55673.2022.10068581","DOIUrl":"https://doi.org/10.1109/ASONAM55673.2022.10068581","url":null,"abstract":"For decades, threat assessment professionals have used structured professional judgment instruments to make decisions about, for example, the likelihood of violent behavior of an individual. However, with the increased use of social media, most people use online digital platforms to communicate, which is also the case for potential violent offenders. For example, many mass shootings in recent years have been preceded by communication in online forums. In this paper, we introduce methods to identify markers of the warning behaviors Leakage, Fixation, Identification, and Affiliation and examine their discriminant validity. Our results show that violent offenders score higher on these markers and that these markers were present among a significantly higher proportion of violent offenders as compared to the normal population. We argue that our method can be used to predict potential planned, purposeful, or instrumental targeted violence in written communication. Automated methods for detecting warning behavior from written communication can serve as a complement to traditional threat assessment and provides unique opportunities for threat assessment beyond traditional methods.","PeriodicalId":423113,"journal":{"name":"2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"21 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":"126673730","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}

{"title":"Universal Graph Embedding Fine Tuning with Dirichlet Energy Smoothing","authors":"Tomi Wójtowicz","doi":"10.1109/ASONAM55673.2022.10068645","DOIUrl":"https://doi.org/10.1109/ASONAM55673.2022.10068645","url":null,"abstract":"Traditionally, networks are interpreted as discrete entities with nodes connected by links. In this work we propose to interpret networks as fields describing the distribution of certain properties in the multidimensional space. By following the field interpretation of networks, we introduce a universal fine-tuning of node embed dings using the concept of Dirichlet energy smoothing to obtain desirable properties of node embeddings.","PeriodicalId":423113,"journal":{"name":"2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"47 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":"115816664","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}

{"title":"Identification of Key Actor Nodes: A Centrality Measure Ranking Aggregation Approach","authors":"Andreas Kosmatopoulos, K. Loumponias, O. Theodosiadou, T. Tsikrika, S. Vrochidis, Y. Kompatsiaris","doi":"10.1109/ASONAM55673.2022.10068668","DOIUrl":"https://doi.org/10.1109/ASONAM55673.2022.10068668","url":null,"abstract":"The identification of key actors in complex networks has gathered significant interest by virtue of their importance in modern applications. Several of the existing methods employ standard centrality measures to achieve their goal and as a result, one of the main challenges is identifying key actor nodes with high relevance across all such measures. In this work, we propose a model based on the use of graph convolutional networks (GeNs) that retrieves the key actors in a network based on a centrality measure ranking aggregation scheme. We experimentally demonstrate the effectiveness of our solution compared to baseline and state-of-the-art approaches in terms of: i) accuracy, ii) performance compared to standard machine learning approaches, and iii) influence propagation capabilities.","PeriodicalId":423113,"journal":{"name":"2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"84 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":"124895131","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}

{"title":"URLytics: Profiling Forum Users from their Posted URLs","authors":"Ben Treves, Md Rayhanul Masud, M. Faloutsos","doi":"10.1109/ASONAM55673.2022.10068682","DOIUrl":"https://doi.org/10.1109/ASONAM55673.2022.10068682","url":null,"abstract":"Online forums contain a substantial amount of data, but very few studies have focused on mining the URLs posted by users. How can we fully leverage these posted URLs to extract as much information as possible about forum users? We perform a systematic study for extracting as much information as possible about forum users via their URL posting behavior. Within this study we develop a series of tools to analyze the data. Given a forum, we extract the following information: (a) basic statistics and a profile of the forum, (b) a profile for each user based on their referral to accounts in other platforms, (c) identification of communities within the forum, and (d) detection of malicious behavior. Most prior works focus on analyzing the text found in user posts rather than on URLs themselves, as we do here. In our study, we analyze three online security forums and find interesting results: (a) we identify 7% of the users posting social media links on other platforms, (b) we detect 148 groups of users that engage in communities on external social media platforms, (c) we expose 139 malicious users that collectively posted 328 malicious URLs. Additionally, we identify 17 groups with membership spanning across multiple forums, and discover numerous other groups that engage in coordinated malicious behavior. Our work is a significant step towards an all-encompassing system for profiling forum users at large.","PeriodicalId":423113,"journal":{"name":"2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"16 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":"125221702","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}

{"title":"Early Detection of Multilingual Troll Accounts on Twitter","authors":"Lin Miao, Mark Last, M. Litvak","doi":"10.1109/ASONAM55673.2022.10068705","DOIUrl":"https://doi.org/10.1109/ASONAM55673.2022.10068705","url":null,"abstract":"Internet troll farms have recently been employed as a powerful and prevailing weapon of information warfare. Even though different tactics may be utilized by different groups of state-sponsored trolls, our goal is to leverage identified troll data for revealing new emerging trolls generating multilingual content. In this work, we adopt a model agnostic meta-learning framework making use of previously released troll farm datasets for the early detection of newly-emerged troll accounts from identified or unidentified troll farms. The detection earliness of various models is evaluated using variable amounts of the earliest tweets from the tested accounts. To evaluate the proposed meta-model, we compare it to several classification models based on different types of account features. Our experiments demonstrate the effectiveness of the meta-model requiring as few as ten tweets to detect a troll account with an average accuracy of 94%.","PeriodicalId":423113,"journal":{"name":"2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"87 11 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":"130327890","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}

{"title":"IKEA: Unsupervised domain-specific keyword-expansion","authors":"Joobin Gharibshah, Jakapun Tachaiya, Arman Irani, E. Papalexakis, M. Faloutsos","doi":"10.1109/ASONAM55673.2022.10068656","DOIUrl":"https://doi.org/10.1109/ASONAM55673.2022.10068656","url":null,"abstract":"How can we expand an initial set of keywords with a target domain in mind? A possible application is to use the expanded set of words to search for specific information within the domain of interest. Here, we focus on online forums and specifically security forums. We propose IKEA, an iterative embedding-based approach to expand a set of keywords with a domain in mind. The novelty of our approach is three-fold: (a) we use two similarity expansions in the word-word and post-post spaces, (b) we use an iterative approach in each of these expansions, and (c) we provide a flexible ranking of the identified words to meet the user needs. We evaluate our method with data from three security forums that span five years of activity and the widely-used Fire benchmark. IKEA outperforms previous solutions by identifying more relevant keywords: it exhibits more than 0.82 MAP and 0.85 NDCG in a wide range of initial keyword sets. We see our approach as an essential building block in developing methods for harnessing the wealth of information available in online forums.","PeriodicalId":423113,"journal":{"name":"2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"3 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":"130001663","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}

{"title":"Understanding the Impact of Culture in Assessing Helpfulness of Online Reviews","authors":"Khaled Alanezi, Nuha Albadi, Omar Hammad, Maram Kurdi, Shivakant Mishra","doi":"10.1109/ASONAM55673.2022.10068664","DOIUrl":"https://doi.org/10.1109/ASONAM55673.2022.10068664","url":null,"abstract":"Online reviews have become essential for users to make informed decisions in everyday tasks ranging from planning summer vacations to purchasing groceries and making financial investments. A key problem in using online reviews is the overabundance of online that overwhelms the users. As a result, recommendation systems for providing helpfulness of reviews are being developed. This paper argues that cultural background is an important feature that impacts the nature of a review written by the user, and must be considered as a feature in assessing the helpfulness of online reviews. The paper provides an in-depth study of differences in online reviews written by users from different cultural backgrounds and how incorporating culture as a feature can lead to better review helpfulness recommendations. In particular, we analyze online reviews originating from two distinct cultural spheres, namely Arabic and Western cultures, for two different products, hotels and books. Our analysis demonstrates that the nature of reviews written by users differs based on their cultural backgrounds and that this difference varies based on the specific product being reviewed. Finally, we have developed six different review helpfulness recommendation models that demonstrate that taking culture into account leads to better recommendations.","PeriodicalId":423113,"journal":{"name":"2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"12 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":"129321133","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}

{"title":"A Machine Learning Approach to Identify Toxic Language in the Online Space","authors":"Lisa Kaati, A. Shrestha, N. Akrami","doi":"10.1109/ASONAM55673.2022.10068619","DOIUrl":"https://doi.org/10.1109/ASONAM55673.2022.10068619","url":null,"abstract":"In this study, we trained three machine learning models to detect toxic language on social media. These models were trained using data from diverse sources to ensure that the models have a broad understanding of toxic language. Next, we evaluate the performance of our models on a dataset with samples of data from a large number of diverse online forums. The test dataset was annotated by three independent annotators. We also compared the performance of our models with Perspective API - a toxic language detection model created by Jigsaw and Google's Counter Abuse Technology team. The results showed that our classification models performed well on data from the domains they were trained on (Fl = 0.91, 0.91, & 0.84, for the RoBERTa, BERT, & SVM respectively), but the performance decreased when they were tested on annotated data from new domains (Fl = 0.80, 0.61, 0.49, & 0.77, for the RoBERTa, BERT, SVM, & Google perspective, respectively). Finally, we used the best-performing model on the test data (RoBERTa, ROC = 0.86) to examine the frequency (/proportion) of toxic language in 21 diverse forums. The results of these analyses showed that forums for general discussions with moderation (e.g., Alternate history) had much lower proportions of toxic language compared to those with minimal moderation (e.g., 8Kun). Although highlighting the complexity of detecting toxic language, our results show that model performance can be improved by using a diverse dataset when building new models. We conclude by discussing the implication of our findings and some directions for future research.","PeriodicalId":423113,"journal":{"name":"2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"5 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":"128523453","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}