Bots (i.e. automated accounts) involve in social campaigns typically for two obvious reasons: to inorganically sway public opinion and to build social capital exploiting the organic popularity of social campaigns. In the process, bots interact with each other and engage in human activities (e.g. likes, retweets, and following). In this work, we detect a large number of bots interested in politics. We perform multi-aspect (i.e. temporal, textual, and topographical) clustering of bots, and ensemble the clusters to identify campaigns of bots. We observe similarity among the bots in a campaign in various aspects such as temporal correlation, sentimental alignment, and topical grouping. However, we also discover bots compete in gaining attention from humans and occasionally engage in arguments. We classify such bot interactions in two primary groups: agreeing (i.e. positive) and disagreeing (i.e. negative) interactions and develop an automatic interaction classifier to discover novel interactions among bots participating in social campaigns.
{"title":"BotCamp: Bot-driven Interactions in Social Campaigns","authors":"Noor Abu-El-Rub, A. Mueen","doi":"10.1145/3308558.3313420","DOIUrl":"https://doi.org/10.1145/3308558.3313420","url":null,"abstract":"Bots (i.e. automated accounts) involve in social campaigns typically for two obvious reasons: to inorganically sway public opinion and to build social capital exploiting the organic popularity of social campaigns. In the process, bots interact with each other and engage in human activities (e.g. likes, retweets, and following). In this work, we detect a large number of bots interested in politics. We perform multi-aspect (i.e. temporal, textual, and topographical) clustering of bots, and ensemble the clusters to identify campaigns of bots. We observe similarity among the bots in a campaign in various aspects such as temporal correlation, sentimental alignment, and topical grouping. However, we also discover bots compete in gaining attention from humans and occasionally engage in arguments. We classify such bot interactions in two primary groups: agreeing (i.e. positive) and disagreeing (i.e. negative) interactions and develop an automatic interaction classifier to discover novel interactions among bots participating in social campaigns.","PeriodicalId":23013,"journal":{"name":"The World Wide Web Conference","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87529042","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}
The continuing expansion of mobile app ecosystems has attracted lots of efforts from the research community. However, although a large number of research studies have focused on analyzing the corpus of mobile apps and app markets, little is known at a comprehensive level on the evolution of mobile app ecosystems. Because the mobile app ecosystem is continuously evolving over time, understanding the dynamics of app ecosystems could provide unique insights that cannot be achieved through studying a single static snapshot. In this paper, we seek to shed light on the dynamics of mobile app ecosystems. Based on 5.3 million app records (with both app metadata and apks) collected from three snapshots of Google Play over more than three years, we conduct the first study on the evolution of app ecosystems from different aspects. Our results suggest that although the overall ecosystem shows promising progress in regard of app popularity, user ratings, permission usage and privacy policy declaration, there still exists a considerable number of unsolved issues including malicious apps, update issues, third-party tracking threats, improper app promotion behaviors, and spamming/malicious developers. Our study shows that understanding the evolution of mobile app ecosystems can help developers make better decision on developing and releasing apps, provide insights for app markets to identifying misbehaviors, and help mobile users to choose desired apps.
{"title":"Understanding the Evolution of Mobile App Ecosystems: A Longitudinal Measurement Study of Google Play","authors":"Haoyu Wang, Hao Li, Yao Guo","doi":"10.1145/3308558.3313611","DOIUrl":"https://doi.org/10.1145/3308558.3313611","url":null,"abstract":"The continuing expansion of mobile app ecosystems has attracted lots of efforts from the research community. However, although a large number of research studies have focused on analyzing the corpus of mobile apps and app markets, little is known at a comprehensive level on the evolution of mobile app ecosystems. Because the mobile app ecosystem is continuously evolving over time, understanding the dynamics of app ecosystems could provide unique insights that cannot be achieved through studying a single static snapshot. In this paper, we seek to shed light on the dynamics of mobile app ecosystems. Based on 5.3 million app records (with both app metadata and apks) collected from three snapshots of Google Play over more than three years, we conduct the first study on the evolution of app ecosystems from different aspects. Our results suggest that although the overall ecosystem shows promising progress in regard of app popularity, user ratings, permission usage and privacy policy declaration, there still exists a considerable number of unsolved issues including malicious apps, update issues, third-party tracking threats, improper app promotion behaviors, and spamming/malicious developers. Our study shows that understanding the evolution of mobile app ecosystems can help developers make better decision on developing and releasing apps, provide insights for app markets to identifying misbehaviors, and help mobile users to choose desired apps.","PeriodicalId":23013,"journal":{"name":"The World Wide Web Conference","volume":"77 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89010778","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}
Fan Zhou, Zijing Wen, Kunpeng Zhang, Goce Trajcevski, Ting Zhong
We present a novel generative Session-Based Recommendation (SBR) framework, called VAriational SEssion-based Recommendation (VASER) - a non-linear probabilistic methodology allowing Bayesian inference for flexible parameter estimation of sequential recommendations. Instead of directly applying extended Variational AutoEncoders (VAE) to SBR, the proposed method introduces normalizing flows to estimate the probabilistic posterior, which is more effective than the agnostic presumed prior approximation used in existing deep generative recommendation approaches. VASER explores soft attention mechanism to upweight the important clicks in a session. We empirically demonstrate that the proposed model significantly outperforms several state-of-the-art baselines, including the recently-proposed RNN/VAE-based approaches on real-world datasets.
{"title":"Variational Session-based Recommendation Using Normalizing Flows","authors":"Fan Zhou, Zijing Wen, Kunpeng Zhang, Goce Trajcevski, Ting Zhong","doi":"10.1145/3308558.3313615","DOIUrl":"https://doi.org/10.1145/3308558.3313615","url":null,"abstract":"We present a novel generative Session-Based Recommendation (SBR) framework, called VAriational SEssion-based Recommendation (VASER) - a non-linear probabilistic methodology allowing Bayesian inference for flexible parameter estimation of sequential recommendations. Instead of directly applying extended Variational AutoEncoders (VAE) to SBR, the proposed method introduces normalizing flows to estimate the probabilistic posterior, which is more effective than the agnostic presumed prior approximation used in existing deep generative recommendation approaches. VASER explores soft attention mechanism to upweight the important clicks in a session. We empirically demonstrate that the proposed model significantly outperforms several state-of-the-art baselines, including the recently-proposed RNN/VAE-based approaches on real-world datasets.","PeriodicalId":23013,"journal":{"name":"The World Wide Web Conference","volume":"63 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84828258","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}
To improve mobile application (App for short) user experience, it is very important to inform the users about the apps' privacy risk levels. To address the challenge of incorporating the heterogeneous feature indicators (such as app permissions, user review, developers' description and ads library) into the risk ranking model, we formalize the app risk ranking problem as an exclusive sparse coding optimization problem by taking advantage of features from different modalities via the maximization of the feature consistency and enhancement of feature diversity. We propose an efficient iterative re-weighted method to solve the resultant optimization problem, the convergence of which can be rigorously proved. The extensive experiments demonstrate the consistent performance improvement using the real-world mobile application datasets (totally 13786 apps, 37966 descriptions, 10557681 user reviews and 200 ad libraries).
{"title":"Mobile App Risk Ranking via Exclusive Sparse Coding","authors":"Deguang Kong, Lei Cen","doi":"10.1145/3308558.3313589","DOIUrl":"https://doi.org/10.1145/3308558.3313589","url":null,"abstract":"To improve mobile application (App for short) user experience, it is very important to inform the users about the apps' privacy risk levels. To address the challenge of incorporating the heterogeneous feature indicators (such as app permissions, user review, developers' description and ads library) into the risk ranking model, we formalize the app risk ranking problem as an exclusive sparse coding optimization problem by taking advantage of features from different modalities via the maximization of the feature consistency and enhancement of feature diversity. We propose an efficient iterative re-weighted method to solve the resultant optimization problem, the convergence of which can be rigorously proved. The extensive experiments demonstrate the consistent performance improvement using the real-world mobile application datasets (totally 13786 apps, 37966 descriptions, 10557681 user reviews and 200 ad libraries).","PeriodicalId":23013,"journal":{"name":"The World Wide Web Conference","volume":"64 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79573366","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}
A. Moniruzzaman, R. Nayak, Maolin Tang, Thirunavukarasu Balasubramaniam
Knowledge Graphs (KGs) have been proven to be incredibly useful for enriching semantic Web search results and allowing queries with a well-defined result set. In recent years much attention has been given to the task of inferring missing facts based on existing facts in a KG. Approaches have also been proposed for inferring types of entities, however these are successful in common types such as 'Person', 'Movie', or 'Actor'. There is still a large gap, however, in the inference of fine-grained types which are highly important for exploring specific lists and collections within web search. Generally there are also relatively fewer observed instances of fine-grained types present to train in KGs, and this poses challenges for the development of effective approaches. In order to address the issue, this paper proposes a new approach to the fine-grained type inference problem. This new approach is explicitly modeled for leveraging domain knowledge and utilizing additional data outside KG, that improves performance in fine-grained type inference. Further improvements in efficiency are achieved by extending the model to probabilistic inference based on entity similarity and typed class classification. We conduct extensive experiments on type triple classification and entity prediction tasks on Freebase FB15K benchmark dataset. The experiment results show that the proposed model outperforms the state-of-the-art approaches for type inference in KG, and achieves high performance results in many-to-one relation in predicting tail for KG completion task.
{"title":"Fine-grained Type Inference in Knowledge Graphs via Probabilistic and Tensor Factorization Methods","authors":"A. Moniruzzaman, R. Nayak, Maolin Tang, Thirunavukarasu Balasubramaniam","doi":"10.1145/3308558.3313597","DOIUrl":"https://doi.org/10.1145/3308558.3313597","url":null,"abstract":"Knowledge Graphs (KGs) have been proven to be incredibly useful for enriching semantic Web search results and allowing queries with a well-defined result set. In recent years much attention has been given to the task of inferring missing facts based on existing facts in a KG. Approaches have also been proposed for inferring types of entities, however these are successful in common types such as 'Person', 'Movie', or 'Actor'. There is still a large gap, however, in the inference of fine-grained types which are highly important for exploring specific lists and collections within web search. Generally there are also relatively fewer observed instances of fine-grained types present to train in KGs, and this poses challenges for the development of effective approaches. In order to address the issue, this paper proposes a new approach to the fine-grained type inference problem. This new approach is explicitly modeled for leveraging domain knowledge and utilizing additional data outside KG, that improves performance in fine-grained type inference. Further improvements in efficiency are achieved by extending the model to probabilistic inference based on entity similarity and typed class classification. We conduct extensive experiments on type triple classification and entity prediction tasks on Freebase FB15K benchmark dataset. The experiment results show that the proposed model outperforms the state-of-the-art approaches for type inference in KG, and achieves high performance results in many-to-one relation in predicting tail for KG completion task.","PeriodicalId":23013,"journal":{"name":"The World Wide Web Conference","volume":"30 8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89482188","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}
Thomas Pellissier Tanon, Camille Bourgaux, Fabian M. Suchanek
The curation of a knowledge base is a crucial but costly task. In this work, we propose to take advantage of the edit history of the knowledge base in order to learn how to correct constraint violations. Our method is based on rule mining, and uses the edits that solved some violations in the past to infer how to solve similar violations in the present. The experimental evaluation of our method on Wikidata shows significant improvements over baselines.
{"title":"Learning How to Correct a Knowledge Base from the Edit History","authors":"Thomas Pellissier Tanon, Camille Bourgaux, Fabian M. Suchanek","doi":"10.1145/3308558.3313584","DOIUrl":"https://doi.org/10.1145/3308558.3313584","url":null,"abstract":"The curation of a knowledge base is a crucial but costly task. In this work, we propose to take advantage of the edit history of the knowledge base in order to learn how to correct constraint violations. Our method is based on rule mining, and uses the edits that solved some violations in the past to infer how to solve similar violations in the present. The experimental evaluation of our method on Wikidata shows significant improvements over baselines.","PeriodicalId":23013,"journal":{"name":"The World Wide Web Conference","volume":"33 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90811709","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}
Li Chen, Y. Yang, Ningxia Wang, Keping Yang, Quan Yuan
Recommendation serendipity is being increasingly recognized as being equally important as the other beyond-accuracy objectives (such as novelty and diversity), in eliminating the “filter bubble” phenomenon of the traditional recommender systems. However, little work has empirically verified the effects of serendipity on increasing user satisfaction and behavioral intention. In this paper, we report the results of a large-scale user survey (involving over 3,000 users) conducted in an industrial mobile e-commerce setting. The study has identified the significant causal relationships from novelty, unexpectedness, relevance, and timeliness to serendipity, and from serendipity to user satisfaction and purchase intention. Moreover, our findings reveal that user curiosity plays a moderating role in strengthening the relationships from novelty to serendipity and from serendipity to satisfaction. Our third contribution lies in the comparison of several recommender algorithms, which demonstrates the significant improvements of the serendipity-oriented algorithm over the relevance- and novelty-oriented approaches in terms of user perceptions. We finally discuss the implications of this experiment, which include the feasibility of developing a more precise metric for measuring recommendation serendipity, and the potential benefit of a curiosity-based personalized serendipity strategy for recommender systems.
{"title":"How Serendipity Improves User Satisfaction with Recommendations? A Large-Scale User Evaluation","authors":"Li Chen, Y. Yang, Ningxia Wang, Keping Yang, Quan Yuan","doi":"10.1145/3308558.3313469","DOIUrl":"https://doi.org/10.1145/3308558.3313469","url":null,"abstract":"Recommendation serendipity is being increasingly recognized as being equally important as the other beyond-accuracy objectives (such as novelty and diversity), in eliminating the “filter bubble” phenomenon of the traditional recommender systems. However, little work has empirically verified the effects of serendipity on increasing user satisfaction and behavioral intention. In this paper, we report the results of a large-scale user survey (involving over 3,000 users) conducted in an industrial mobile e-commerce setting. The study has identified the significant causal relationships from novelty, unexpectedness, relevance, and timeliness to serendipity, and from serendipity to user satisfaction and purchase intention. Moreover, our findings reveal that user curiosity plays a moderating role in strengthening the relationships from novelty to serendipity and from serendipity to satisfaction. Our third contribution lies in the comparison of several recommender algorithms, which demonstrates the significant improvements of the serendipity-oriented algorithm over the relevance- and novelty-oriented approaches in terms of user perceptions. We finally discuss the implications of this experiment, which include the feasibility of developing a more precise metric for measuring recommendation serendipity, and the potential benefit of a curiosity-based personalized serendipity strategy for recommender systems.","PeriodicalId":23013,"journal":{"name":"The World Wide Web Conference","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91147246","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}
Fan Yang, Shiva K. Pentyala, Sina Mohseni, Mengnan Du, Hao Yuan, Rhema Linder, E. Ragan, Shuiwang Ji, Xia Hu
In this demo paper, we present the XFake system, an explainable fake news detector that assists end-users to identify news credibility. To effectively detect and interpret the fakeness of news items, we jointly consider both attributes (e.g., speaker) and statements. Specifically, MIMIC, ATTN and PERT frameworks are designed, where MIMIC is built for attribute analysis, ATTN is for statement semantic analysis and PERT is for statement linguistic analysis. Beyond the explanations extracted from the designed frameworks, relevant supporting examples as well as visualization are further provided to facilitate the interpretation. Our implemented system is demonstrated on a real-world dataset crawled from PolitiFact1, where thousands of verified political news have been collected.
{"title":"XFake: Explainable Fake News Detector with Visualizations","authors":"Fan Yang, Shiva K. Pentyala, Sina Mohseni, Mengnan Du, Hao Yuan, Rhema Linder, E. Ragan, Shuiwang Ji, Xia Hu","doi":"10.1145/3308558.3314119","DOIUrl":"https://doi.org/10.1145/3308558.3314119","url":null,"abstract":"In this demo paper, we present the XFake system, an explainable fake news detector that assists end-users to identify news credibility. To effectively detect and interpret the fakeness of news items, we jointly consider both attributes (e.g., speaker) and statements. Specifically, MIMIC, ATTN and PERT frameworks are designed, where MIMIC is built for attribute analysis, ATTN is for statement semantic analysis and PERT is for statement linguistic analysis. Beyond the explanations extracted from the designed frameworks, relevant supporting examples as well as visualization are further provided to facilitate the interpretation. Our implemented system is demonstrated on a real-world dataset crawled from PolitiFact1, where thousands of verified political news have been collected.","PeriodicalId":23013,"journal":{"name":"The World Wide Web Conference","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89730950","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}
Jeffery Ansah, Lin Liu, Wei Kang, Selasi Kwashie, Jixue Li, Jiuyong Li
Story timeline summarization is widely used by analysts, law enforcement agencies, and policymakers for content presentation, story-telling, and other data-driven decision-making applications. Recent advancements in web technologies have rendered social media sites such as Twitter and Facebook as a viable platform for discovering evolving stories and trending events for story timeline summarization. However, a timeline summarization structure that models complex evolving stories by tracking event evolution to identify different themes of a story and generate a coherent structure that is easy for users to understand is yet to be explored. In this paper, we propose StoryGraph, a novel graph timeline summarization structure that is capable of identifying the different themes of a story. By using high penalty metrics that leverage user network communities, temporal proximity, and the semantic context of the events, we construct coherent paths and generate structural timeline summaries to tell the story of how events evolve over time. We performed experiments on real-world datasets to show the prowess of StoryGraph. StoryGraph outperforms existing models and produces accurate timeline summarizations. As a key finding, we discover that user network communities increase coherence leading to the generation of consistent summary structures.
{"title":"A Graph is Worth a Thousand Words: Telling Event Stories using Timeline Summarization Graphs","authors":"Jeffery Ansah, Lin Liu, Wei Kang, Selasi Kwashie, Jixue Li, Jiuyong Li","doi":"10.1145/3308558.3313396","DOIUrl":"https://doi.org/10.1145/3308558.3313396","url":null,"abstract":"Story timeline summarization is widely used by analysts, law enforcement agencies, and policymakers for content presentation, story-telling, and other data-driven decision-making applications. Recent advancements in web technologies have rendered social media sites such as Twitter and Facebook as a viable platform for discovering evolving stories and trending events for story timeline summarization. However, a timeline summarization structure that models complex evolving stories by tracking event evolution to identify different themes of a story and generate a coherent structure that is easy for users to understand is yet to be explored. In this paper, we propose StoryGraph, a novel graph timeline summarization structure that is capable of identifying the different themes of a story. By using high penalty metrics that leverage user network communities, temporal proximity, and the semantic context of the events, we construct coherent paths and generate structural timeline summaries to tell the story of how events evolve over time. We performed experiments on real-world datasets to show the prowess of StoryGraph. StoryGraph outperforms existing models and produces accurate timeline summarizations. As a key finding, we discover that user network communities increase coherence leading to the generation of consistent summary structures.","PeriodicalId":23013,"journal":{"name":"The World Wide Web Conference","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75122053","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}
Cryptocurrencies are a novel and disruptive technology that has prompted a new approach to how currencies work in the modern economy. As such, online discussions related to cryptocurrencies often go beyond posts about the technology and underlying architecture of the various coins, to subjective speculations of price fluctuations and predictions. Furthermore, online discussions, potentially driven by foreign adversaries, criminals or hackers, can have a significant impact on our economy and national security if spread at scale. This paper is the first to qualitatively measure and contrast discussion growth about three popular cryptocurrencies with key distinctions in motivation, usage, and implementation - Bitcoin, Ethereum, and Monero on Reddit. More specifically, we measure how discussions relevant to these coins spread in online social environments - how deep and how wide they go, how long they last, how many people they reach, etc. More importantly, we compare user behavior patterns between the focused community of the official coin subreddits and the general community across Reddit as a whole. Our Reddit sample covers three years of data between 2015 and 2018 and includes a time period of a record high Bitcoin price rise.1 Our results demonstrate that while the largest discussions on Reddit are focused on Bitcoin, posts about Monero (a cryptocurrency often used by criminals for illegal transactions on the Dark Web2) start discussions that are typically longer and wider. Bitcoin posts trigger subsequent discussion more immediately but Monero posts are more likely to trigger a longer lasting discussion. We find that moderately subjective posts across all three coins trigger larger, longer, and more viral discussion cascades within both focused and general communities on Reddit. Our analysis aims to bring the awareness to online discussion spread relevant to cryptocurrencies in addition to informing models for forecasting cryptocurrency price that rely on discussions in social media.
{"title":"Characterizing Speed and Scale of Cryptocurrency Discussion Spread on Reddit","authors":"M. Glenski, Emily Saldanha, Svitlana Volkova","doi":"10.1145/3308558.3313702","DOIUrl":"https://doi.org/10.1145/3308558.3313702","url":null,"abstract":"Cryptocurrencies are a novel and disruptive technology that has prompted a new approach to how currencies work in the modern economy. As such, online discussions related to cryptocurrencies often go beyond posts about the technology and underlying architecture of the various coins, to subjective speculations of price fluctuations and predictions. Furthermore, online discussions, potentially driven by foreign adversaries, criminals or hackers, can have a significant impact on our economy and national security if spread at scale. This paper is the first to qualitatively measure and contrast discussion growth about three popular cryptocurrencies with key distinctions in motivation, usage, and implementation - Bitcoin, Ethereum, and Monero on Reddit. More specifically, we measure how discussions relevant to these coins spread in online social environments - how deep and how wide they go, how long they last, how many people they reach, etc. More importantly, we compare user behavior patterns between the focused community of the official coin subreddits and the general community across Reddit as a whole. Our Reddit sample covers three years of data between 2015 and 2018 and includes a time period of a record high Bitcoin price rise.1 Our results demonstrate that while the largest discussions on Reddit are focused on Bitcoin, posts about Monero (a cryptocurrency often used by criminals for illegal transactions on the Dark Web2) start discussions that are typically longer and wider. Bitcoin posts trigger subsequent discussion more immediately but Monero posts are more likely to trigger a longer lasting discussion. We find that moderately subjective posts across all three coins trigger larger, longer, and more viral discussion cascades within both focused and general communities on Reddit. Our analysis aims to bring the awareness to online discussion spread relevant to cryptocurrencies in addition to informing models for forecasting cryptocurrency price that rely on discussions in social media.","PeriodicalId":23013,"journal":{"name":"The World Wide Web Conference","volume":"58 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75257896","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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