2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)最新文献

{"title":"BeSober: Assisting relapse prevention in Alcohol Addiction using a novel mobile app-based intervention","authors":"Vinay Jayachandra Reddy, Rashmi Kesidi, Zhou Yang, Chen Zhang, Zhenhe Pan, Victor S. Sheng, Fang Jin","doi":"10.1109/ASONAM49781.2020.9381364","DOIUrl":"https://doi.org/10.1109/ASONAM49781.2020.9381364","url":null,"abstract":"Chronic alcohol consumption has become one of the major concerns of the society in the sector of public health. The death toll due to excessive consumption of alcohol is increasing exponentially. Face-to-face interaction to create awareness and to stymie the consumption of alcohol is a quixotic solution. Alcohol relapse remains a challenging problem in disorders associated with alcohol addiction which is related to spatial-temporal factors like periods and specific places. In this paper, a new generation of relapse prevention mobile application called “BeSober” is proposed. It assists users to develop abstemious habits in drinking, providing support within the intervention period, tracking risky alcoholic spots, presenting community-based support from alternative users or alcohol addiction therapists, monitoring addict's behaviour and offer personalized recommendations to assist the addict in staying sober.","PeriodicalId":196317,"journal":{"name":"2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126710687","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":"Characterizing (Un)moderated Textual Data in Social Systems","authors":"Lucas Lima, Júlio Cesar dos Reis, P. Melo, Fabricio Murai, Fabrício Benevenuto","doi":"10.1109/ASONAM49781.2020.9381327","DOIUrl":"https://doi.org/10.1109/ASONAM49781.2020.9381327","url":null,"abstract":"Despite the valuable social interactions that online media promote, these systems provide space for speech that would be potentially detrimental to different groups of people. The moderation of content imposed by many social media has motivated the emergence of a new social system for free speech named Gab, which lacks moderation of content. This article characterizes and compares moderated textual data from Twitter with a set of unmoderated data from Gab. In particular, we analyze distinguishing characteristics of moderated and unmoderated content in terms of linguistic features, evaluate hate speech and its different forms in both environments. Our work shows that unmoderated content presents different psycholinguistic features, more negative sentiment and higher toxicity. Our findings support that unmoderated environments may have proportionally more online hate speech. We hope our analysis and findings contribute to the debate about hate speech and benefit systems aiming at deploying hate speech detection approaches.","PeriodicalId":196317,"journal":{"name":"2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"55 43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122335411","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":"Bias in Knowledge Graph Embeddings","authors":"Styliani Bourli, E. Pitoura","doi":"10.1109/ASONAM49781.2020.9381459","DOIUrl":"https://doi.org/10.1109/ASONAM49781.2020.9381459","url":null,"abstract":"In this paper, we study bias in knowledge graph embeddings. We focus on gender bias in occupations, but our approach is applicable to other types of bias. We start by proposing measures for identifying bias in the dataset (i.e., in the KG) and then present two methods for testing whether any bias in the dataset is amplified by the embeddings. First, we look for gender-specific occupation analogies in the embeddings. Second, we test whether link prediction (i.e., occupation prediction in our case) aggregates gender bias by proposing gender-dominated occupations to people of the corresponding gender more often than expected. Then, we use a debiasing approach based on projections on the gender subspace. We present experimental results using the Wikidata dataset and pretrained TransE embeddings. Our results show that there exists gender bias in the dataset and that such bias is amplified by the embeddings. Our debiasing approach removes bias with a small penalty on accuracy.","PeriodicalId":196317,"journal":{"name":"2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134028394","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":"Network and Revenue of the Clube Hurb Affiliate Marketing Program: A Story of Two Tales","authors":"Lucas L. Rolim, J. E. Simões, D. R. Figueiredo","doi":"10.1109/ASONAM49781.2020.9381447","DOIUrl":"https://doi.org/10.1109/ASONAM49781.2020.9381447","url":null,"abstract":"Individuals in affiliate marketing programs sign up with companies to promote or sell their products in independent venues and channels, receiving compensations for their actions. While a component of the e-commerce ecosystem for over a decade, affiliate marketing is increasingly being adopted by companies given its promises of boosting revenue at low investment costs. This work analyzes Clube Hurb, the affiliate marketing program of Hurb.com the largest online travel agency in Brazil. The analysis reveals the fragility of social network growth (very low virality coefficient) along with the strength of social referrals. It also reveals that almost all revenue generated by the program comes from a small set of affiliates, a property that has sustained over time. Indeed, great disparities are characterized by heavy-tailed distributions in statistics concerning both the network and revenue structure. Thus, while most affiliates play no effective role, a few are instrumental in keeping the program profitable. Such findings indicate that traditional average-based performance metrics can be flawed when assessing the success of such programs.","PeriodicalId":196317,"journal":{"name":"2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115026807","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 Twitter Social Contagion Monitor","authors":"Vladimir Barash, Clayton Fink, C. Cameron, Aurora C. Schmidt, Wei Dong, M. Macy, John Kelly, Amruta Deshpande","doi":"10.1109/ASONAM49781.2020.9381313","DOIUrl":"https://doi.org/10.1109/ASONAM49781.2020.9381313","url":null,"abstract":"We describe and validate a system for monitoring social contagions on Twitter: social movements, rumors, and emotional outbursts that spread from person to person in a viral manner. We use Twitter streams to monitor the spread of these phenomena through human social and information networks. This system, the contagion monitor, parses Twitter posts to identify emerging phenomena, as captured in hashtags, URLs, words and phrases, or account-handles, and then determines the extent to which a particular phenomenon spreads via the social network (in contrast to its spread via news broadcasts or independent adoption) and locates the contagion within Twitter communities. The monitor approximates the adoption threshold of a social contagion by measuring the fraction of Twitter users who were “infected” by the contagion (e.g., joined a particular social movement) after more than one of their friends had done so. Finally, the monitor makes a judgment about whether the phenomenon has reached critical mass, which is defined as the point where a social contagion begins spreading rapidly and breaches the social boundaries of its early adopter group. We test our prototype monitor on two data sources — an ongoing stream of tweets grouped by user-added hashtags and a collection of posts by a monitored set of Nigerian Twitter users — before productionalizing. We use the Amazon Mechanical Turk platform to evaluate the performance on both data sources. In both cases, we find that our approach successfully distinguishes between high-threshold and low-threshold social contagions.","PeriodicalId":196317,"journal":{"name":"2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"84 12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124383127","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":"TrustGCN: Enabling Graph Convolutional Network for Robust Sybil Detection in OSNs","authors":"Yue Sun, Zhi Yang, Yafei Dai","doi":"10.1109/ASONAM49781.2020.9381325","DOIUrl":"https://doi.org/10.1109/ASONAM49781.2020.9381325","url":null,"abstract":"Detecting fake accounts (also called Sybils) is a fundamental security problem in online social networks (OSNs). Existing feature-based or social-graph-based approaches suffer from the key limitations: they can only leverage either node feature or graph structure properties such as fast-mixing and conductance, but not both. To overcome this shortcoming, we explore the introduction of recent advancements in deep neural networks for graph-structured data into Sybil detection field. These types of models enable integrating both user-level activities and graph-level structures for a new generation of feature-and-graph-based detection mechanisms. However, we find that although applying Graph Convolutional Networks (GCNs) are effective against naïve attacks, they are vulnerable to adversarial attacks in which fake accounts alter local edges and features with patterns to resemble real users. In this paper, we present TrustGCN, a Sybil-resilient defense algorithm that combines the idea of social-graph-based defense with GCN. TrustGCN first assigns trust scores to nodes based on the landing probability of short random walks that starts from known real accounts. As this short, supervised random walk is likely to stay within the subgraph consisting of real accounts, most real accounts receive higher trust scores than fakes. Then it introduces these trust scores as edge weights and adopts graph convolution operations to aggregate features of local graph neighborhoods over this weighted graph for classification. In this way, we prevent Sybil partners with low trust scores from contributing to the feature aggregation for a target node, thus is more robust against adverse manipulations of the attackers. Our experiment on real data demonstrates that TrustGCN significantly outperforms GCN in the robustness. To the best of our knowledge, this is the first attempt to combine social-graph-based defenses with graph neural networks into a unified model, paving the way for the robust feature-and-graph-based detection mechanisms.","PeriodicalId":196317,"journal":{"name":"2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"214 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126102760","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":"Dynamics of the international environmental treaties - perspectives for future cooperation","authors":"A. Nita, L. Rozylowicz","doi":"10.1109/ASONAM49781.2020.9381333","DOIUrl":"https://doi.org/10.1109/ASONAM49781.2020.9381333","url":null,"abstract":"International treaties and multilateral agreements are undoubtedly based on networks, which, considering the magnitude of the environmental issues or resources conflicts that we are facing, become complex networks. Implementing a strategy that easily integrates all these problems is particularly difficult to develop or apply, and the disengagement has no way to help. To achieve successful environmental governance is only possible with the involvement of all parties or stakeholders. This paper illustrates the evolution of the cooperation network established between the international parties that ratified the most important environmental treaties at international level discussing transboundary issues. By applying a network analysis perspective, we explore the dynamics of the cooperation considering 3-time intervals, namely: collaboration for the implementation of the treaties before 1990 (1), before 2000 (2), and before 2020 (i.e., the cooperation established so far within the most common environmental agreements). We further examine the network structure by investigating the core-periphery model, which shows the current situation in terms of level of involvement in the ratification and application of the principles of the international environmental treaties established. Our findings suggest that a complex and more functional system is needed to manage both common biodiversity resources and solve existing transboundary environmental conflicts.","PeriodicalId":196317,"journal":{"name":"2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124686148","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":"Analysis and Prediction of COVID-19 Timeline and Infection Rates","authors":"H. Al-Mubaid, I. Alsmadi","doi":"10.1109/ASONAM49781.2020.9381338","DOIUrl":"https://doi.org/10.1109/ASONAM49781.2020.9381338","url":null,"abstract":"The number of new cases of infection of the Coronavirus disease, COVID-19, is alarming in many places in the world. In several world countries, including USA, the infection rates and daily cases numbers are fairly high; and there are even some spike increases in some USA states. Since the USA is experiencing the highest number of daily new cases in the world from May through July, the most important question is when we will witness an effective decline in the number of daily new cases? This paper follows a data-driven approach to induce the disease decline values from two country groups in the world where the disease declined already to less than 25% of its peak daily new cases. We apply these country groups' models to predict the decline to 25% of the US's peak. We compiled, examined, and analyzed pandemic data and statistics of two countries: g1: 42 countries, and g2: 14 countries. We utilize their data in the prediction of the decline timeline of the US. Group g2 consists of 14 countries having a similar number of cases per one million population. The majority of the models predict that the decline to 25% of the US's peak will be around the end of November to the first week of October. The results are significant and impressive as it is highly demanded to have clues and methods for the timeline prediction of this pandemic in the USA.","PeriodicalId":196317,"journal":{"name":"2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127286616","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":"Assessing e-Recruiting on Social Media: FBI Case Study","authors":"I. McCulloh, Nathan Ellis, O. Savas, Paul Rodrigues","doi":"10.1109/ASONAM49781.2020.9381351","DOIUrl":"https://doi.org/10.1109/ASONAM49781.2020.9381351","url":null,"abstract":"With the rise in popularity of social media, these platforms present a new opportunity to reach potential job candidates for employment opportunities. The current literature lacks sufficient research on methods and best practices to design and assess the efficacy of recruit and hire campaigns delivered on social media. We present a case study of a government e-recruiting effort discovered on Twitter. We collected almost 20 thousand tweets using the hashtag #FBIJobs, this included both Tweets and Retweets. Applications of descriptive statistics, topic modeling, sentiment analysis, and graph analytics identify where the campaign may miss potentially interested job candidates. We also find evidence of “popularity transfer” where co-mentions appear to increase the visibility of an accounts content in public feeds, without transferring the sentiment surrounding the more popular account. The research and findings were based on a publicly available e-recruiting campaign found online, without any inside knowledge or influence on campaign design or execution. Recommendations to better focus e-recruiting campaigns are provided.","PeriodicalId":196317,"journal":{"name":"2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"141 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125890500","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":"International Symposium on Foundations of Open Source Intelligence and Security Informatics (FOSINT-SI 2020) FOSINT-SI 2020 Symposium Organizing Committee","authors":"D. Skillicorn, U. Glasser, Simon Fraser, Robyn Torok, E. Cowan, V. Balas, A. Vlaicu, F. Gurgen, Alan Wang, J. Park, Roozbeh Farahbod, J. Piskorski, M. Hengst, A. J. Hoogstrate, Xiaolong Zheng","doi":"10.1109/asonam49781.2020.9381331","DOIUrl":"https://doi.org/10.1109/asonam49781.2020.9381331","url":null,"abstract":"","PeriodicalId":196317,"journal":{"name":"2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125906881","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}