Finding clusters in a network has been practically important in many applications and was studied by many researchers. Most commonly used methods are spectral clustering and Newman's modularity maximization. However, there has been no unified view of them. In this study, we introduced a new guiding principle based on correspondence analysis to obtain nodes' coordinates and discussed its equivalence to spectral clustering and its relationship to Newman's modularity.
{"title":"CAB-NC: The Correspondence Analysis Based Network Clustering Method","authors":"M. Kimura","doi":"10.1145/3341161.3342944","DOIUrl":"https://doi.org/10.1145/3341161.3342944","url":null,"abstract":"Finding clusters in a network has been practically important in many applications and was studied by many researchers. Most commonly used methods are spectral clustering and Newman's modularity maximization. However, there has been no unified view of them. In this study, we introduced a new guiding principle based on correspondence analysis to obtain nodes' coordinates and discussed its equivalence to spectral clustering and its relationship to Newman's modularity.","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121166020","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}
In asset allocation and time-series forecasting studies, few have shed light on using the different machine learning and deep learning models to verify the difference in the result of investment returns and optimal asset allocation. To fill this research gap, we develop a robo-advisor with different machine learning and deep learning forecasting methodologies and utilize the forecasting result of the portfolio optimization model to support our investors in making decisions. This research integrated several dimensions of technologies, which contain machine learning, data analytics, and portfolio optimization. We focused on developing robo-advisor framework and utilized algorithms by integrating machine learning and deep learning approaches with the portfolio optimization algorithm by using our predicted trends and results to replace the historical data and investor views. We eliminate the extreme fluctuation to maintain our trading within the acceptable risk coefficient. Accordingly, we can minimize the investment risk and reach a relatively stable return. We compared different algorithms and found that the F1 score of the model prediction significantly affects the result of the optimized portfolio. We used our deep learning model with the highest winning rate and leveraged the prediction result with the portfolio optimization algorithm to reach 12% of annual return, which outperform our benchmark index 0050. TW and the optimized portfolio with the integration of historical data.
{"title":"Artificial Intelligence for ETF Market Prediction and Portfolio Optimization","authors":"Min-Yuh Day, Jian-Ting Lin","doi":"10.1145/3341161.3344822","DOIUrl":"https://doi.org/10.1145/3341161.3344822","url":null,"abstract":"In asset allocation and time-series forecasting studies, few have shed light on using the different machine learning and deep learning models to verify the difference in the result of investment returns and optimal asset allocation. To fill this research gap, we develop a robo-advisor with different machine learning and deep learning forecasting methodologies and utilize the forecasting result of the portfolio optimization model to support our investors in making decisions. This research integrated several dimensions of technologies, which contain machine learning, data analytics, and portfolio optimization. We focused on developing robo-advisor framework and utilized algorithms by integrating machine learning and deep learning approaches with the portfolio optimization algorithm by using our predicted trends and results to replace the historical data and investor views. We eliminate the extreme fluctuation to maintain our trading within the acceptable risk coefficient. Accordingly, we can minimize the investment risk and reach a relatively stable return. We compared different algorithms and found that the F1 score of the model prediction significantly affects the result of the optimized portfolio. We used our deep learning model with the highest winning rate and leveraged the prediction result with the portfolio optimization algorithm to reach 12% of annual return, which outperform our benchmark index 0050. TW and the optimized portfolio with the integration of historical data.","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130034595","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}
In this study, a procedure is proposed for surveying public opinion from big social media domain-specific textual data to minimize the difficulties associated with modeling public behavior. Strategies for labeling posts relevant to a topic are discussed. A two-part framework is proposed in which semiautomatic labeling is applied to a small subset of posts, referred to as the “seed” in further text. This seed is used as bases for semi-supervised labeling of the rest of the data. The hypothesis is that the proposed method will achieve better labeling performance than existing classification models when applied to small amounts of labeled data. The seed is labeled using posts of users with a known and consistent view on the topic. A semi-supervised multi-class prediction model labels the remaining data iteratively. In each iteration, it adds context-label pairs to the training set if softmax-based label probabilities are above the threshold. The proposed method is characterized on four datasets by comparison to the three popular text modeling algorithms (n-grams + tfidf, fastText, VDCNN) for different sizes of labeled seeds (5,000 and 50,000 posts) and for several label-prediction significance thresholds. Our proposed semi-supervised method outperformed alternative algorithms by capturing additional contexts from the unlabeled data. The accuracy of the algorithm was increasing by (3-10%) when using a larger fraction of data as the seed. For the smaller seed, lower label probability threshold was clearly a better choice, while for larger seeds no predominant threshold was observed. The proposed framework, using fastText library for efficient text classification and representation learning, achieved the best results for a smaller seed, while VDCNN wrapped in the proposed framework achieved the best results for the bigger seed. The performance was negatively influenced by the number of classes. Finally, the model was applied to characterize a biased dataset of opinions related to gun control/rights advocacy. The proposed semi-automatic seed labeling is used to label 8,448 twitter posts of 171 advocates for guns control/rights. On this application, our approach performed better than existing models and it achieves 96.5% accuracy and 0.68 F1 score.
{"title":"Surveying public opinion using label prediction on social media data","authors":"Marija Stanojevic, Jumanah Alshehri, Z. Obradovic","doi":"10.1145/3341161.3342861","DOIUrl":"https://doi.org/10.1145/3341161.3342861","url":null,"abstract":"In this study, a procedure is proposed for surveying public opinion from big social media domain-specific textual data to minimize the difficulties associated with modeling public behavior. Strategies for labeling posts relevant to a topic are discussed. A two-part framework is proposed in which semiautomatic labeling is applied to a small subset of posts, referred to as the “seed” in further text. This seed is used as bases for semi-supervised labeling of the rest of the data. The hypothesis is that the proposed method will achieve better labeling performance than existing classification models when applied to small amounts of labeled data. The seed is labeled using posts of users with a known and consistent view on the topic. A semi-supervised multi-class prediction model labels the remaining data iteratively. In each iteration, it adds context-label pairs to the training set if softmax-based label probabilities are above the threshold. The proposed method is characterized on four datasets by comparison to the three popular text modeling algorithms (n-grams + tfidf, fastText, VDCNN) for different sizes of labeled seeds (5,000 and 50,000 posts) and for several label-prediction significance thresholds. Our proposed semi-supervised method outperformed alternative algorithms by capturing additional contexts from the unlabeled data. The accuracy of the algorithm was increasing by (3-10%) when using a larger fraction of data as the seed. For the smaller seed, lower label probability threshold was clearly a better choice, while for larger seeds no predominant threshold was observed. The proposed framework, using fastText library for efficient text classification and representation learning, achieved the best results for a smaller seed, while VDCNN wrapped in the proposed framework achieved the best results for the bigger seed. The performance was negatively influenced by the number of classes. Finally, the model was applied to characterize a biased dataset of opinions related to gun control/rights advocacy. The proposed semi-automatic seed labeling is used to label 8,448 twitter posts of 171 advocates for guns control/rights. On this application, our approach performed better than existing models and it achieves 96.5% accuracy and 0.68 F1 score.","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117315266","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}
When performing any analysis task, some information may be leaked or scattered among individuals who may not willing to share their information (e.g., number of individual's friends and who they are). Secure multi-party computation (MPC) allows individuals to jointly perform any computation without revealing each individual's input. Here, we present two novel secure frameworks which allow node to securely compute its clustering coefficient, which we evaluate the trade off between efficiency and security of several proposed instantiations. Our results show that the cost for secure computing highly depends on network structure.
{"title":"Computing Node Clustering Coefficients Securely","authors":"K. Areekijseree, Y. Tang, S. Soundarajan","doi":"10.1145/3341161.3342946","DOIUrl":"https://doi.org/10.1145/3341161.3342946","url":null,"abstract":"When performing any analysis task, some information may be leaked or scattered among individuals who may not willing to share their information (e.g., number of individual's friends and who they are). Secure multi-party computation (MPC) allows individuals to jointly perform any computation without revealing each individual's input. Here, we present two novel secure frameworks which allow node to securely compute its clustering coefficient, which we evaluate the trade off between efficiency and security of several proposed instantiations. Our results show that the cost for secure computing highly depends on network structure.","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114824883","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}
Increasing use of social media in campaigns raises the question of whether one can predict the voting behavior of social-network users who do not disclose their political preferences in their online profiles. Prior work on this task only considered users who generate politically oriented content or voluntarily disclose their political preferences online. We avoid this bias by using a novel Bayesian-network model that combines demographic, behavioral, and social features; we apply this novel approach to the 2016 U.S. Presidential election. Our model is highly extensible and facilitates the use of incomplete datasets. Furthermore, our work is the first to apply a semi-supervised approach for this task: Using the EM algorithm, we combine labeled survey data with unlabeled Facebook data, thus obtaining larger datasets as well as addressing self-selection bias.
{"title":"Show me your friends, and I will tell you whom you vote for: Predicting voting behavior in social networks","authors":"Lihi Idan, J. Feigenbaum","doi":"10.1145/3341161.3343676","DOIUrl":"https://doi.org/10.1145/3341161.3343676","url":null,"abstract":"Increasing use of social media in campaigns raises the question of whether one can predict the voting behavior of social-network users who do not disclose their political preferences in their online profiles. Prior work on this task only considered users who generate politically oriented content or voluntarily disclose their political preferences online. We avoid this bias by using a novel Bayesian-network model that combines demographic, behavioral, and social features; we apply this novel approach to the 2016 U.S. Presidential election. Our model is highly extensible and facilitates the use of incomplete datasets. Furthermore, our work is the first to apply a semi-supervised approach for this task: Using the EM algorithm, we combine labeled survey data with unlabeled Facebook data, thus obtaining larger datasets as well as addressing self-selection bias.","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114647813","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}
F. Motlagh, Saeedeh Shekarpour, A. Sheth, K. Thirunarayan, M. Raymer
In this paper, we focus on the collection and analysis of relevant Twitter data on a state-by-state basis for (i) measuring public opinion on marijuana legalization by mining sentiment in Twitter data and (ii) determining the usage trends for six distinct types of marijuana. We overcome the challenges posed by the informal and ungrammatical nature of tweets to analyze a corpus of 306,835 relevant tweets collected over the four-month period, preceding the November 2015 Ohio Marijuana Legalization ballot and the four months after the election for all states in the US. Our analysis revealed two key insights: (i) the people in states that have legalized recreational marijuana express greater positive sentiments about marijuana than the people in states that have either legalized medicinal marijuana or have not legalized marijuana at all; (ii) the states that have a high percentage of positive sentiment about marijuana is more inclined to authorize (e.g., by allowing medical marijuana) or broaden its legal usage (e.g., by allowing recreational marijuana in addition to medical marijuana). Our analysis shows that social media can provide reliable information and can serve as an alternative to traditional polling of public opinion on drug use and epidemiology research.
{"title":"Predicting Public Opinion on Drug Legalization: Social Media Analysis and Consumption Trends","authors":"F. Motlagh, Saeedeh Shekarpour, A. Sheth, K. Thirunarayan, M. Raymer","doi":"10.1145/3341161.3344380","DOIUrl":"https://doi.org/10.1145/3341161.3344380","url":null,"abstract":"In this paper, we focus on the collection and analysis of relevant Twitter data on a state-by-state basis for (i) measuring public opinion on marijuana legalization by mining sentiment in Twitter data and (ii) determining the usage trends for six distinct types of marijuana. We overcome the challenges posed by the informal and ungrammatical nature of tweets to analyze a corpus of 306,835 relevant tweets collected over the four-month period, preceding the November 2015 Ohio Marijuana Legalization ballot and the four months after the election for all states in the US. Our analysis revealed two key insights: (i) the people in states that have legalized recreational marijuana express greater positive sentiments about marijuana than the people in states that have either legalized medicinal marijuana or have not legalized marijuana at all; (ii) the states that have a high percentage of positive sentiment about marijuana is more inclined to authorize (e.g., by allowing medical marijuana) or broaden its legal usage (e.g., by allowing recreational marijuana in addition to medical marijuana). Our analysis shows that social media can provide reliable information and can serve as an alternative to traditional polling of public opinion on drug use and epidemiology research.","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125985363","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}
Depression is the most common mental illness in the U.S., with 6.7% of all adults who have experienced a major depressive episode. Unfortunately, depression extends to teens and young users as well, and researchers observed an increasing rate in the recent years (from 8.7% in 2005 to 11.3% in 2014 in adolescents and from 8.8% to 9.6% in young adults), especially among girls and women. People themselves are a barrier to fight this disease as they tend to hide their symptoms and do not receive treatments. However, protected by anonymity, they share their sentiments on the Web, looking for help. In this paper, we address the problem of detecting depressed users in online forums. We analyze user behavior in the Rea-chOut.com online forum, a platform providing a supportive environment for young people to discuss their everyday issues, including depression. We examine the linguistic style of user posts in combination with network-based features modeling how users connect in the forum. Our results show that network features are strong predictors of depressed users and, by combining them with user post linguistic features, we can achieve an average precision of 0.78 (vs. 0.47 of a random classifier and 0.71 of linguistic features only) and perform better than related work (F1-measure of 0.63 vs. 0.50).
抑郁症是美国最常见的精神疾病，6.7%的成年人经历过严重的抑郁症发作。不幸的是，青少年和年轻用户也会患上抑郁症，研究人员观察到近年来抑郁症的发病率不断上升(青少年从2005年的8.7%上升到2014年的11.3%，年轻人从8.8%上升到9.6%)，尤其是在女孩和女性中。人们本身是对抗这种疾病的障碍，因为他们往往隐藏自己的症状，不接受治疗。然而，在匿名的保护下，他们在网上分享自己的情绪，寻求帮助。在本文中，我们解决了在在线论坛中检测抑郁用户的问题。我们分析了Rea-chOut.com在线论坛的用户行为，这是一个为年轻人提供支持性环境的平台，可以讨论他们的日常问题，包括抑郁症。我们将用户帖子的语言风格与基于网络的特征相结合，对用户在论坛中的连接方式进行建模。我们的研究结果表明，网络特征是抑郁用户的强大预测因子，通过将它们与用户帖子语言特征相结合，我们可以实现0.78的平均精度(相对于随机分类器的0.47和语言特征的0.71)，并且表现优于相关工作(f1测量值为0.63 vs 0.50)。
{"title":"Detecting Depressed Users in Online Forums","authors":"Anu Shrestha, Francesca Spezzano","doi":"10.1145/3341161.3343511","DOIUrl":"https://doi.org/10.1145/3341161.3343511","url":null,"abstract":"Depression is the most common mental illness in the U.S., with 6.7% of all adults who have experienced a major depressive episode. Unfortunately, depression extends to teens and young users as well, and researchers observed an increasing rate in the recent years (from 8.7% in 2005 to 11.3% in 2014 in adolescents and from 8.8% to 9.6% in young adults), especially among girls and women. People themselves are a barrier to fight this disease as they tend to hide their symptoms and do not receive treatments. However, protected by anonymity, they share their sentiments on the Web, looking for help. In this paper, we address the problem of detecting depressed users in online forums. We analyze user behavior in the Rea-chOut.com online forum, a platform providing a supportive environment for young people to discuss their everyday issues, including depression. We examine the linguistic style of user posts in combination with network-based features modeling how users connect in the forum. Our results show that network features are strong predictors of depressed users and, by combining them with user post linguistic features, we can achieve an average precision of 0.78 (vs. 0.47 of a random classifier and 0.71 of linguistic features only) and perform better than related work (F1-measure of 0.63 vs. 0.50).","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127493836","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}
Before purchasing a product online, customers often read the reviews posted by people who also brought the product. Customer reviews provide opinions and relevant information such as comparisons among similar products or usage experiences about the product. Previous studies addressed on the prediction of the helpfulness of customer reviews to predict the helpfulness voting results. However, the voting result of an online review is not a constant over time; predicting the voting result based on the analysis of text is not practical. Therefore, we collect the voting results of the same online customer review over time, and observe whether the number of votes will increase or not. We construct a dataset with 10,195 online reviews in six different product categories (Computer Hardware, Drink, Makeup, Pen, Shoes, and Toys) from Amazon.cn with the voting result on the helpfulness of the reviews, and monitor the helpfulness voting in six weeks. Experiments are conducted on the dataset to predict whether the helpfulness voting result of each review will increase or not. We propose a classification system that can classify the online reviews into more helpful ones, based on a set of syntactic features and neural features trained via CNN. The results show that integrating the syntactic features with the neural features can get better result.
{"title":"Integrating Neural and Syntactic Features on the Helpfulness Analysis of the Online Customer Reviews","authors":"Shih-Hung Wu, Jun-Wei Wang","doi":"10.1145/3341161.3344825","DOIUrl":"https://doi.org/10.1145/3341161.3344825","url":null,"abstract":"Before purchasing a product online, customers often read the reviews posted by people who also brought the product. Customer reviews provide opinions and relevant information such as comparisons among similar products or usage experiences about the product. Previous studies addressed on the prediction of the helpfulness of customer reviews to predict the helpfulness voting results. However, the voting result of an online review is not a constant over time; predicting the voting result based on the analysis of text is not practical. Therefore, we collect the voting results of the same online customer review over time, and observe whether the number of votes will increase or not. We construct a dataset with 10,195 online reviews in six different product categories (Computer Hardware, Drink, Makeup, Pen, Shoes, and Toys) from Amazon.cn with the voting result on the helpfulness of the reviews, and monitor the helpfulness voting in six weeks. Experiments are conducted on the dataset to predict whether the helpfulness voting result of each review will increase or not. We propose a classification system that can classify the online reviews into more helpful ones, based on a set of syntactic features and neural features trained via CNN. The results show that integrating the syntactic features with the neural features can get better result.","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121745509","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}
Opioid addiction is a severe public health threat in the U.S, causing massive deaths and many social problems. Accurate relapse prediction is of practical importance for recovering patients since relapse prediction promotes timely relapse preventions that help patients stay clean. In this paper, we introduce a Generative Adversarial Networks (GAN) model to predict the addiction relapses based on sentiment images and social influences. Experimental results on real social media data from Reddit.com demonstrate that the GAN model delivers a better performance than comparable alternative techniques. The sentiment images generated by the model show that relapse is closely connected with two emotions ‘joy’ and ‘negative’. This work is one of the first attempts to predict relapses using massive social media (Reddit.com) data and generative adversarial nets. The proposed method, combined with knowledge of social media mining, has the potential to revolutionize the practice of opioid addiction prevention and treatment.
{"title":"Opioid Relapse Prediction with GAN","authors":"Zhou Yang, L. Nguyen, Fang Jin","doi":"10.1145/3341161.3342951","DOIUrl":"https://doi.org/10.1145/3341161.3342951","url":null,"abstract":"Opioid addiction is a severe public health threat in the U.S, causing massive deaths and many social problems. Accurate relapse prediction is of practical importance for recovering patients since relapse prediction promotes timely relapse preventions that help patients stay clean. In this paper, we introduce a Generative Adversarial Networks (GAN) model to predict the addiction relapses based on sentiment images and social influences. Experimental results on real social media data from Reddit.com demonstrate that the GAN model delivers a better performance than comparable alternative techniques. The sentiment images generated by the model show that relapse is closely connected with two emotions ‘joy’ and ‘negative’. This work is one of the first attempts to predict relapses using massive social media (Reddit.com) data and generative adversarial nets. The proposed method, combined with knowledge of social media mining, has the potential to revolutionize the practice of opioid addiction prevention and treatment.","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124983962","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}
Rating platforms provide users with useful information on products or other users. However, fake ratings are sometimes generated by fraudulent users. In this paper, we tackle the task of fraudulent user detection on rating platforms. We propose an end-to-end framework based on Graph Convolutional Networks (GCNs) and expanded balance theory, which properly incorporates both the signs and directions of edges. Experimental results on four real-world datasets show that the proposed framework performs better, or even best, in most settings. In particular, this framework shows remarkable stability in inductive settings, which is associated with the detection of new fraudulent users on rating platforms. Furthermore, using expanded balance theory, we provide new insight into the behavior of users in rating networks, that fraudulent users form a faction to deal with the negative ratings from other users. The owner of a rating platform can detect fraudulent users earlier and constantly provide users with more credible information by using the proposed framework.
{"title":"Fraudulent User Detection on Rating Networks Based on Expanded Balance Theory and GCNs","authors":"Wataru Kudo, Mao Nishiguchi, F. Toriumi","doi":"10.1145/3341161.3342929","DOIUrl":"https://doi.org/10.1145/3341161.3342929","url":null,"abstract":"Rating platforms provide users with useful information on products or other users. However, fake ratings are sometimes generated by fraudulent users. In this paper, we tackle the task of fraudulent user detection on rating platforms. We propose an end-to-end framework based on Graph Convolutional Networks (GCNs) and expanded balance theory, which properly incorporates both the signs and directions of edges. Experimental results on four real-world datasets show that the proposed framework performs better, or even best, in most settings. In particular, this framework shows remarkable stability in inductive settings, which is associated with the detection of new fraudulent users on rating platforms. Furthermore, using expanded balance theory, we provide new insight into the behavior of users in rating networks, that fraudulent users form a faction to deal with the negative ratings from other users. The owner of a rating platform can detect fraudulent users earlier and constantly provide users with more credible information by using the proposed framework.","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125110211","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}
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