Microblogs have become the preferred means of communication for people to share information and feelings, especially for fast evolving events. Understanding the emotional reactions of people allows decision makers to formulate policies that are likely to be more well-received by the public and hence better accepted especially during policy implementation. However, uncovering the topics and emotions related to an event over time is a challenge due to the short and noisy nature of microblogs. This work proposes a weakly supervised learning approach to learn coherent topics and the corresponding emotional reactions as an event unfolds. We summarize the event by giving the representative microblogs and the emotion distributions associated with the topics over time. Experiments on multiple real-world event datasets demonstrate the effectiveness of the proposed approach over existing solutions.
{"title":"Emotion-Aware Event Summarization in Microblogs","authors":"R. Panchendrarajan, W. Hsu, M. Lee","doi":"10.1145/3442442.3452311","DOIUrl":"https://doi.org/10.1145/3442442.3452311","url":null,"abstract":"Microblogs have become the preferred means of communication for people to share information and feelings, especially for fast evolving events. Understanding the emotional reactions of people allows decision makers to formulate policies that are likely to be more well-received by the public and hence better accepted especially during policy implementation. However, uncovering the topics and emotions related to an event over time is a challenge due to the short and noisy nature of microblogs. This work proposes a weakly supervised learning approach to learn coherent topics and the corresponding emotional reactions as an event unfolds. We summarize the event by giving the representative microblogs and the emotion distributions associated with the topics over time. Experiments on multiple real-world event datasets demonstrate the effectiveness of the proposed approach over existing solutions.","PeriodicalId":129420,"journal":{"name":"Companion Proceedings of the Web Conference 2021","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121213942","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}
Christopher Stelzmüller, Sebastian Tanzer, M. Schedl
Geolocated user-generated content is a promising source of data reflecting how citizens live and feel. Information extracted from this source is being increasingly used for urban planning and policy evaluation purposes. While a lot of existing research focuses on the relationship between locations and sentiment in social media postings, we aim to uncover relations between location and sentiment that are consistent over cities around the world. In this paper, we therefore analyze the relationship between multiple categories of points of interest (POIs) in the OpenStreetMap dataset and the sentiment of English microblogging messages sent nearby using a three-stage processing pipeline: (1) extract sentiment scores from geolocated microblogs posted on Twitter, (2) spatial aggregation of sentiment in cities and POIs, (3) analyze relationships in aggregated sentiment. We identify differences in Twitter users’ sentiments within cities based on POIs, and we investigate the temporal dynamics of these sentiments and compare our findings between major cities in multiple countries.
{"title":"Cross-city Analysis of Location-based Sentiment in User-generated Text","authors":"Christopher Stelzmüller, Sebastian Tanzer, M. Schedl","doi":"10.1145/3442442.3451889","DOIUrl":"https://doi.org/10.1145/3442442.3451889","url":null,"abstract":"Geolocated user-generated content is a promising source of data reflecting how citizens live and feel. Information extracted from this source is being increasingly used for urban planning and policy evaluation purposes. While a lot of existing research focuses on the relationship between locations and sentiment in social media postings, we aim to uncover relations between location and sentiment that are consistent over cities around the world. In this paper, we therefore analyze the relationship between multiple categories of points of interest (POIs) in the OpenStreetMap dataset and the sentiment of English microblogging messages sent nearby using a three-stage processing pipeline: (1) extract sentiment scores from geolocated microblogs posted on Twitter, (2) spatial aggregation of sentiment in cities and POIs, (3) analyze relationships in aggregated sentiment. We identify differences in Twitter users’ sentiments within cities based on POIs, and we investigate the temporal dynamics of these sentiments and compare our findings between major cities in multiple countries.","PeriodicalId":129420,"journal":{"name":"Companion Proceedings of the Web Conference 2021","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128698357","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}
Sree Sai Teja Lanka, S. Rajtmajer, Jian Wu, C. Lee Giles
With substantial and continuing increases in the number of published papers across the scientific literature, development of reliable approaches for automated discovery and assessment of published findings is increasingly urgent. Tools which can extract critical information from scientific papers and metadata can support representation and reasoning over existing findings, and offer insights into replicability, robustness and generalizability of specific claims. In this work, we present a pipeline for the extraction of statistical information (p-values, sample size, number of hypotheses tested) from full-text scientific documents. We validate our approach on 300 papers selected from the social and behavioral science literatures, and suggest directions for next steps.
{"title":"Extraction and Evaluation of Statistical Information from Social and Behavioral Science Papers","authors":"Sree Sai Teja Lanka, S. Rajtmajer, Jian Wu, C. Lee Giles","doi":"10.1145/3442442.3451363","DOIUrl":"https://doi.org/10.1145/3442442.3451363","url":null,"abstract":"With substantial and continuing increases in the number of published papers across the scientific literature, development of reliable approaches for automated discovery and assessment of published findings is increasingly urgent. Tools which can extract critical information from scientific papers and metadata can support representation and reasoning over existing findings, and offer insights into replicability, robustness and generalizability of specific claims. In this work, we present a pipeline for the extraction of statistical information (p-values, sample size, number of hypotheses tested) from full-text scientific documents. We validate our approach on 300 papers selected from the social and behavioral science literatures, and suggest directions for next steps.","PeriodicalId":129420,"journal":{"name":"Companion Proceedings of the Web Conference 2021","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116332836","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}
Large-scale social networks have become more and more popular with the rapid progress of social media. A number of social network analysis tasks have been developed to conduct on the real large-scale networks. However, the prohibitive cost of achieving the underlying large network, including time cost and data privacy, makes it hard to evaluate the performance of analysis algorithms on real-world social networks. In this paper, we present a tool called FastSNG, which generates heterogeneous social network datasets according to the user-defined configuration depicting the rich characteristics of the expected social network, such as community structures, attributes, and node degree distributions. Moreover, the generation algorithm of FastSNG adopts a degree distribution generation (D2G) model which is efficient to generate web-scale social network datasets. Finally, the tool provides user-friendly and succinct user interfaces for the interaction with general users.
{"title":"FastSNG: The Fastest Social Network Dataset Generator","authors":"Binbin Wang, Chaokun Wang, Hao Feng","doi":"10.1145/3442442.3458604","DOIUrl":"https://doi.org/10.1145/3442442.3458604","url":null,"abstract":"Large-scale social networks have become more and more popular with the rapid progress of social media. A number of social network analysis tasks have been developed to conduct on the real large-scale networks. However, the prohibitive cost of achieving the underlying large network, including time cost and data privacy, makes it hard to evaluate the performance of analysis algorithms on real-world social networks. In this paper, we present a tool called FastSNG, which generates heterogeneous social network datasets according to the user-defined configuration depicting the rich characteristics of the expected social network, such as community structures, attributes, and node degree distributions. Moreover, the generation algorithm of FastSNG adopts a degree distribution generation (D2G) model which is efficient to generate web-scale social network datasets. Finally, the tool provides user-friendly and succinct user interfaces for the interaction with general users.","PeriodicalId":129420,"journal":{"name":"Companion Proceedings of the Web Conference 2021","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115239576","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}
Dirk Ahlers, Erik Wilde, R. Schifanella, Jalal S. Alowibdi
LocWeb2021 (Eleventh International Workshop on Location and the Web) is a workshop at The Web Conference 2021, with evolving topics around location-aware information access, Web architecture, spatial social computing, and social good. It is designed as a meeting place for researchers around the location topic at The Web Conference.
{"title":"LocWeb2021 Workshop – Chair’s Welcome: Eleventh International Workshop on Location and the Web at The Web Conference 2021","authors":"Dirk Ahlers, Erik Wilde, R. Schifanella, Jalal S. Alowibdi","doi":"10.1145/3442442.3451891","DOIUrl":"https://doi.org/10.1145/3442442.3451891","url":null,"abstract":"LocWeb2021 (Eleventh International Workshop on Location and the Web) is a workshop at The Web Conference 2021, with evolving topics around location-aware information access, Web architecture, spatial social computing, and social good. It is designed as a meeting place for researchers around the location topic at The Web Conference.","PeriodicalId":129420,"journal":{"name":"Companion Proceedings of the Web Conference 2021","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126752834","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}
Chang-Ryong Jung, I. Hong, Diego Sáez-Trumper, Damin Lee, Jaehyeon Myung, Danu Kim, Jinhyuk Yun, Woo-Sung Jung, M. Cha
Wikipedia has been a critical information source during the COVID-19 pandemic. Analyzing how information is created, edited, and viewed on this platform can help gain new insights for risk communication strategies for the next pandemic. Here, we study the content editor and viewer patterns on the COVID-19 related documents on Wikipedia using a near-complete dataset gathered of 11 languages over 238 days in 2020. Based on the analysis of the daily access and edit logs on the identified Wikipedia pages, we discuss how the regional and cultural closeness factors affect information demand and supply.
{"title":"Information flow on COVID-19 over Wikipedia: A case study of 11 languages","authors":"Chang-Ryong Jung, I. Hong, Diego Sáez-Trumper, Damin Lee, Jaehyeon Myung, Danu Kim, Jinhyuk Yun, Woo-Sung Jung, M. Cha","doi":"10.1145/3442442.3452352","DOIUrl":"https://doi.org/10.1145/3442442.3452352","url":null,"abstract":"Wikipedia has been a critical information source during the COVID-19 pandemic. Analyzing how information is created, edited, and viewed on this platform can help gain new insights for risk communication strategies for the next pandemic. Here, we study the content editor and viewer patterns on the COVID-19 related documents on Wikipedia using a near-complete dataset gathered of 11 languages over 238 days in 2020. Based on the analysis of the daily access and edit logs on the identified Wikipedia pages, we discuss how the regional and cultural closeness factors affect information demand and supply.","PeriodicalId":129420,"journal":{"name":"Companion Proceedings of the Web Conference 2021","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126781003","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}
Shashank Kedia, Aditya Mantha, Sneha R. Gupta, Stephen D. Guo, Kannan Achan
Through recent advancements in speech technologies and introduction of smart assistants, such as Amazon Alexa, Apple Siri and Google Home, increasing number of users are interacting with various applications through voice commands. E-commerce companies typically display short product titles on their webpages, either human-curated or algorithmically generated, when brevity is required. However, these titles are dissimilar from natural spoken language. For example, ”Lucky Charms Gluten Free Break-fast Cereal, 20.5 oz a box Lucky Charms Gluten Free” is acceptable to display on a webpage, while a similar title cannot be used in a voice based text-to-speech application. In such conversational systems, an easy to comprehend sentence, such as ”a 20.5 ounce box of lucky charms gluten free cereal” is preferred. Compared to display devices, where images and detailed product information can be presented to users, short titles for products which convey the most important information, are necessary when interfacing with voice assistants. We propose eBERT, a sequence-to-sequence approach by further pre-training the BERT embeddings on an e-commerce product description corpus, and then fine-tuning the resulting model to generate short, natural, spoken language titles from input web titles. Our extensive experiments on a real-world industry dataset, as well as human evaluation of model output, demonstrate that eBERT summarization outperforms comparable baseline models. Owing to the efficacy of the model, a version of this model has been deployed in real-world setting.
{"title":"Generating Rich Product Descriptions for Conversational E-commerce Systems","authors":"Shashank Kedia, Aditya Mantha, Sneha R. Gupta, Stephen D. Guo, Kannan Achan","doi":"10.1145/3442442.3451893","DOIUrl":"https://doi.org/10.1145/3442442.3451893","url":null,"abstract":"Through recent advancements in speech technologies and introduction of smart assistants, such as Amazon Alexa, Apple Siri and Google Home, increasing number of users are interacting with various applications through voice commands. E-commerce companies typically display short product titles on their webpages, either human-curated or algorithmically generated, when brevity is required. However, these titles are dissimilar from natural spoken language. For example, ”Lucky Charms Gluten Free Break-fast Cereal, 20.5 oz a box Lucky Charms Gluten Free” is acceptable to display on a webpage, while a similar title cannot be used in a voice based text-to-speech application. In such conversational systems, an easy to comprehend sentence, such as ”a 20.5 ounce box of lucky charms gluten free cereal” is preferred. Compared to display devices, where images and detailed product information can be presented to users, short titles for products which convey the most important information, are necessary when interfacing with voice assistants. We propose eBERT, a sequence-to-sequence approach by further pre-training the BERT embeddings on an e-commerce product description corpus, and then fine-tuning the resulting model to generate short, natural, spoken language titles from input web titles. Our extensive experiments on a real-world industry dataset, as well as human evaluation of model output, demonstrate that eBERT summarization outperforms comparable baseline models. Owing to the efficacy of the model, a version of this model has been deployed in real-world setting.","PeriodicalId":129420,"journal":{"name":"Companion Proceedings of the Web Conference 2021","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127134669","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}
Many online services, including search engines, content delivery networks, ad networks, and fraud detection utilize IP geolocation databases to map IP addresses to their physical locations. However, IP geolocation databases are often inaccurate. We present a novel IP geolocation technique based on combining propagating IP location information through traceroutes with IP interpolation. Using a large ground truth set, we show that physical locations of IP addresses can be propagated along traceroute paths. We also experiment with and expand upon the concept of IP range location interpolation, where we use the location of individual addresses in an IP range to assign a location to the entire range. The results show that our approach significantly outperforms commercial geolocation by up to 31 percentage points. We open source several components to aid in reproducing our results.
{"title":"IP Geolocation Using Traceroute Location Propagation and IP Range Location Interpolation","authors":"Ovidiu Dan, Vaibhav Parikh, Brian D. Davison","doi":"10.1145/3442442.3451888","DOIUrl":"https://doi.org/10.1145/3442442.3451888","url":null,"abstract":"Many online services, including search engines, content delivery networks, ad networks, and fraud detection utilize IP geolocation databases to map IP addresses to their physical locations. However, IP geolocation databases are often inaccurate. We present a novel IP geolocation technique based on combining propagating IP location information through traceroutes with IP interpolation. Using a large ground truth set, we show that physical locations of IP addresses can be propagated along traceroute paths. We also experiment with and expand upon the concept of IP range location interpolation, where we use the location of individual addresses in an IP range to assign a location to the entire range. The results show that our approach significantly outperforms commercial geolocation by up to 31 percentage points. We open source several components to aid in reproducing our results.","PeriodicalId":129420,"journal":{"name":"Companion Proceedings of the Web Conference 2021","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124839887","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}
Jürgen Hackl, Ingo Scholtes, L. V. Petrovic, Vincenzo Perri, Luca Verginer, Christoph Gote
The Open Source software package pathpy, available at https://www.pathpy.net, implements statistical techniques to learn optimal graphical models for the causal topology generated by paths in time-series data. Operationalizing Occam’s razor, these models balance model complexity with explanatory power for empirically observed paths in relational time series. Standard network analysis is justified if the inferred optimal model is a first-order network model. Optimal models with orders larger than one indicate higher-order dependencies and can be used to improve the analysis of dynamical processes, node centralities and clusters.
{"title":"Analysis and Visualisation of Time Series Data on Networks with Pathpy","authors":"Jürgen Hackl, Ingo Scholtes, L. V. Petrovic, Vincenzo Perri, Luca Verginer, Christoph Gote","doi":"10.1145/3442442.3452052","DOIUrl":"https://doi.org/10.1145/3442442.3452052","url":null,"abstract":"The Open Source software package pathpy, available at https://www.pathpy.net, implements statistical techniques to learn optimal graphical models for the causal topology generated by paths in time-series data. Operationalizing Occam’s razor, these models balance model complexity with explanatory power for empirically observed paths in relational time series. Standard network analysis is justified if the inferred optimal model is a first-order network model. Optimal models with orders larger than one indicate higher-order dependencies and can be used to improve the analysis of dynamical processes, node centralities and clusters.","PeriodicalId":129420,"journal":{"name":"Companion Proceedings of the Web Conference 2021","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124285312","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}
Measuring the quality of research work is an essential component of the scientific process. With the ever-growing rates of articles being submitted to top-tier conferences, and the potential consistency and bias issues in the peer review process identified by scientific community, it is thus of great necessary and challenge to automatically evaluate submissions. Existing works mainly focus on exploring relevant factors and applying machine learning models to simply be accurate at predicting the acceptance of a given academic paper, while ignoring the interpretability power which is required by a wide range of applications. In this paper, we propose a framework to construct decision sets that consist of unordered if-then rules for predicting paper acceptance. We formalize decision set learning problem via a joint objective function that simultaneously optimize accuracy and interpretability of the rules, rather than organizing them in a hierarchy. We evaluate the effectiveness of the proposed framework by applying it on a public scientific peer reviews dataset. Experimental results demonstrate that the learned interpretable decision sets by our framework performs on par with state-of-the-art classification algorithms which optimize exclusively for predictive accuracy and much more interpretable than rule-based methods.
{"title":"Predicting Paper Acceptance via Interpretable Decision Sets","authors":"Peng Bao, Weihui Hong, Xuanya Li","doi":"10.1145/3442442.3451370","DOIUrl":"https://doi.org/10.1145/3442442.3451370","url":null,"abstract":"Measuring the quality of research work is an essential component of the scientific process. With the ever-growing rates of articles being submitted to top-tier conferences, and the potential consistency and bias issues in the peer review process identified by scientific community, it is thus of great necessary and challenge to automatically evaluate submissions. Existing works mainly focus on exploring relevant factors and applying machine learning models to simply be accurate at predicting the acceptance of a given academic paper, while ignoring the interpretability power which is required by a wide range of applications. In this paper, we propose a framework to construct decision sets that consist of unordered if-then rules for predicting paper acceptance. We formalize decision set learning problem via a joint objective function that simultaneously optimize accuracy and interpretability of the rules, rather than organizing them in a hierarchy. We evaluate the effectiveness of the proposed framework by applying it on a public scientific peer reviews dataset. Experimental results demonstrate that the learned interpretable decision sets by our framework performs on par with state-of-the-art classification algorithms which optimize exclusively for predictive accuracy and much more interpretable than rule-based methods.","PeriodicalId":129420,"journal":{"name":"Companion Proceedings of the Web Conference 2021","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123312747","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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