{"title":"HT '22: 33rd ACM Conference on Hypertext and Social Media, Barcelona, Spain, 28 June 2022- 1 July 2022","authors":"","doi":"10.1145/3511095","DOIUrl":"https://doi.org/10.1145/3511095","url":null,"abstract":"","PeriodicalId":91270,"journal":{"name":"HT ... : the proceedings of the ... ACM Conference on Hypertext and Social Media. ACM Conference on Hypertext and Social Media","volume":"31 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86256155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"HT '21: 32nd ACM Conference on Hypertext and Social Media, Virtual Event, Ireland, 30 August 2021 - 2 September 2021","authors":"","doi":"10.1145/3465336","DOIUrl":"https://doi.org/10.1145/3465336","url":null,"abstract":"","PeriodicalId":91270,"journal":{"name":"HT ... : the proceedings of the ... ACM Conference on Hypertext and Social Media. ACM Conference on Hypertext and Social Media","volume":"59 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84473413","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"HT '20: 31st ACM Conference on Hypertext and Social Media, Virtual Event, USA, July 13-15, 2020","authors":"","doi":"10.1145/3372923","DOIUrl":"https://doi.org/10.1145/3372923","url":null,"abstract":"","PeriodicalId":91270,"journal":{"name":"HT ... : the proceedings of the ... ACM Conference on Hypertext and Social Media. ACM Conference on Hypertext and Social Media","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74758209","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}
Mrinal Kumar, Mark Dredze, Glen Coppersmith, Munmun De Choudhury
The Werther effect describes the increased rate of completed or attempted suicides following the depiction of an individual's suicide in the media, typically a celebrity. We present findings on the prevalence of this effect in an online platform: r/SuicideWatch on Reddit. We examine both the posting activity and post content after the death of ten high-profile suicides. Posting activity increases following reports of celebrity suicides, and post content exhibits considerable changes that indicate increased suicidal ideation. Specifically, we observe that post-celebrity suicide content is more likely to be inward focused, manifest decreased social concerns, and laden with greater anxiety, anger, and negative emotion. Topic model analysis further reveals content in this period to switch to a more derogatory tone that bears evidence of self-harm and suicidal tendencies. We discuss the implications of our findings in enabling better community support to psychologically vulnerable populations, and the potential of building suicide prevention interventions following high-profile suicides.
{"title":"Detecting Changes in Suicide Content Manifested in Social Media Following Celebrity Suicides.","authors":"Mrinal Kumar, Mark Dredze, Glen Coppersmith, Munmun De Choudhury","doi":"10.1145/2700171.2791026","DOIUrl":"https://doi.org/10.1145/2700171.2791026","url":null,"abstract":"<p><p>The Werther effect describes the increased rate of completed or attempted suicides following the depiction of an individual's suicide in the media, typically a celebrity. We present findings on the prevalence of this effect in an online platform: r/SuicideWatch on Reddit. We examine both the posting activity and post content after the death of ten high-profile suicides. Posting activity increases following reports of celebrity suicides, and post content exhibits considerable changes that indicate increased suicidal ideation. Specifically, we observe that post-celebrity suicide content is more likely to be inward focused, manifest decreased social concerns, and laden with greater anxiety, anger, and negative emotion. Topic model analysis further reveals content in this period to switch to a more derogatory tone that bears evidence of self-harm and suicidal tendencies. We discuss the implications of our findings in enabling better community support to psychologically vulnerable populations, and the potential of building suicide prevention interventions following high-profile suicides.</p>","PeriodicalId":91270,"journal":{"name":"HT ... : the proceedings of the ... ACM Conference on Hypertext and Social Media. ACM Conference on Hypertext and Social Media","volume":"2015 ","pages":"85-94"},"PeriodicalIF":0.0,"publicationDate":"2015-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/2700171.2791026","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35175985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Wikipedia is commonly used as a proving ground for research in collaborative systems. This is likely due to its popularity and scale, but also to the fact that large amounts of data about its formation and evolution are freely available to inform and validate theories and models of online collaboration. As part of the development of such approaches, revert detection is often performed as an important pre-processing step in tasks as diverse as the extraction of implicit networks of editors, the analysis of edit or editor features and the removal of noise when analyzing the emergence of the content of an article. The current state of the art in revert detection is based on a rather naive approach, which identifies revision duplicates based on MD5 hash values. This is an efficient, but not very precise technique that forms the basis for the majority of research based on revert relations in Wikipedia. In this paper we prove that this method has a number of important drawbacks - it only detects a limited number of reverts, while simultaneously misclassifying too many edits as reverts, and not distinguishing between complete and partial reverts. This is very likely to hamper the accurate interpretation of the findings of revert-related research. We introduce an improved algorithm for the detection of reverts based on word tokens added or deleted to adresses these drawbacks. We report on the results of a user study and other tests demonstrating the considerable gains in accuracy and coverage by our method, and argue for a positive trade-off, in certain research scenarios, between these improvements and our algorithm's increased runtime.
{"title":"Revisiting reverts: accurate revert detection in wikipedia","authors":"Fabian Flöck, Denny Vrandečić, E. Simperl","doi":"10.1145/2309996.2310000","DOIUrl":"https://doi.org/10.1145/2309996.2310000","url":null,"abstract":"Wikipedia is commonly used as a proving ground for research in collaborative systems. This is likely due to its popularity and scale, but also to the fact that large amounts of data about its formation and evolution are freely available to inform and validate theories and models of online collaboration. As part of the development of such approaches, revert detection is often performed as an important pre-processing step in tasks as diverse as the extraction of implicit networks of editors, the analysis of edit or editor features and the removal of noise when analyzing the emergence of the content of an article. The current state of the art in revert detection is based on a rather naive approach, which identifies revision duplicates based on MD5 hash values. This is an efficient, but not very precise technique that forms the basis for the majority of research based on revert relations in Wikipedia. In this paper we prove that this method has a number of important drawbacks - it only detects a limited number of reverts, while simultaneously misclassifying too many edits as reverts, and not distinguishing between complete and partial reverts. This is very likely to hamper the accurate interpretation of the findings of revert-related research. We introduce an improved algorithm for the detection of reverts based on word tokens added or deleted to adresses these drawbacks. We report on the results of a user study and other tests demonstrating the considerable gains in accuracy and coverage by our method, and argue for a positive trade-off, in certain research scenarios, between these improvements and our algorithm's increased runtime.","PeriodicalId":91270,"journal":{"name":"HT ... : the proceedings of the ... ACM Conference on Hypertext and Social Media. ACM Conference on Hypertext and Social Media","volume":"45 1","pages":"3-12"},"PeriodicalIF":0.0,"publicationDate":"2012-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78716436","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}
Giovanni V. Comarela, M. Crovella, Virgílio A. F. Almeida, Fabrício Benevenuto
In information networks where users send messages to one another, the issue of information overload naturally arises: which are the most important messages? In this paper we study the problem of understanding the importance of messages in Twitter. We approach this problem in two stages. First, we perform an extensive characterization of a very large Twitter dataset which includes all users, social relations, and messages posted from the beginning of the service up to August 2009. We show evidence that information overload is present: users sometimes have to search through hundreds of messages to find those that are interesting to reply or retweet. We then identify factors that influence user response or retweet probability: previous responses to the same tweeter, the tweeter's sending rate, the age and some basic text elements of the tweet. In our second stage, we show that some of these factors can be used to improve the presentation order of tweets to the user. First, by inspecting user activity over time, we construct a simple on-off model of user behavior that allows us to infer when a user is actively using Twitter. Then, we explore two methods from machine learning for ranking tweets: a Naive Bayes predictor and a Support Vector Machine classifier. We show that it is possible to reorder tweets to increase the fraction of replied or retweeted messages appearing in the first p positions of the list by as much as 50-60%.
{"title":"Understanding factors that affect response rates in twitter","authors":"Giovanni V. Comarela, M. Crovella, Virgílio A. F. Almeida, Fabrício Benevenuto","doi":"10.1145/2309996.2310017","DOIUrl":"https://doi.org/10.1145/2309996.2310017","url":null,"abstract":"In information networks where users send messages to one another, the issue of information overload naturally arises: which are the most important messages? In this paper we study the problem of understanding the importance of messages in Twitter. We approach this problem in two stages. First, we perform an extensive characterization of a very large Twitter dataset which includes all users, social relations, and messages posted from the beginning of the service up to August 2009. We show evidence that information overload is present: users sometimes have to search through hundreds of messages to find those that are interesting to reply or retweet. We then identify factors that influence user response or retweet probability: previous responses to the same tweeter, the tweeter's sending rate, the age and some basic text elements of the tweet. In our second stage, we show that some of these factors can be used to improve the presentation order of tweets to the user. First, by inspecting user activity over time, we construct a simple on-off model of user behavior that allows us to infer when a user is actively using Twitter. Then, we explore two methods from machine learning for ranking tweets: a Naive Bayes predictor and a Support Vector Machine classifier. We show that it is possible to reorder tweets to increase the fraction of replied or retweeted messages appearing in the first p positions of the list by as much as 50-60%.","PeriodicalId":91270,"journal":{"name":"HT ... : the proceedings of the ... ACM Conference on Hypertext and Social Media. ACM Conference on Hypertext and Social Media","volume":"4 1","pages":"123-132"},"PeriodicalIF":0.0,"publicationDate":"2012-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73310703","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}
Public Administration organizations generally write their citizen-focused, informational materials for generic audiences because they don't have the resources to produce personalized materials for everyone. The goal of this project is to replace these generic materials, which must include careful discussions of the conditions distinguishing the various constituencies within the generic audience, with tailored materials, which can be automatically personalized to focus on the information relevant to an individual reader. Two key questions must be addressed. First, are the automatically produced, tailored forms more effective than the generic forms they replace, and second, is the time the reader spends specifying the demographic information on which the tailoring is based too costly to be worth the effort. This paper describes an adaptive hypermedia application that produces tailored materials for students exploring government educational entitlement programs, and focuses in particular on the effectiveness of the generated tailored material.
{"title":"An evaluation of tailored web materials for public administration","authors":"N. Colineau, Cécile Paris, Keith Vander Linden","doi":"10.1145/2309996.2310040","DOIUrl":"https://doi.org/10.1145/2309996.2310040","url":null,"abstract":"Public Administration organizations generally write their citizen-focused, informational materials for generic audiences because they don't have the resources to produce personalized materials for everyone. The goal of this project is to replace these generic materials, which must include careful discussions of the conditions distinguishing the various constituencies within the generic audience, with tailored materials, which can be automatically personalized to focus on the information relevant to an individual reader. Two key questions must be addressed. First, are the automatically produced, tailored forms more effective than the generic forms they replace, and second, is the time the reader spends specifying the demographic information on which the tailoring is based too costly to be worth the effort. This paper describes an adaptive hypermedia application that produces tailored materials for students exploring government educational entitlement programs, and focuses in particular on the effectiveness of the generated tailored material.","PeriodicalId":91270,"journal":{"name":"HT ... : the proceedings of the ... ACM Conference on Hypertext and Social Media. ACM Conference on Hypertext and Social Media","volume":"114 1","pages":"265-274"},"PeriodicalIF":0.0,"publicationDate":"2012-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76672680","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}
Online critical literacy challenges readers to recognize and question how online textual information has been shaped by its greater context. While comparing information from multiple sources provides a foundation for such awareness, keeping pace with everything being written is a daunting proposition, especially for the casual reader. We propose a new form of technological assistance for critical literacy which automatically discovers and displays underlying memes: ideas represented by similar phrases which occur across diýerent information sources. By surfacing these memes to users, we create a rich hypertext representation in which underlying memes can be explored in context. Given the vast scale of social media, we describe a highly-scalable system architecture designed for MapReduce distributed computing. To validate our approach, we report on use of our system to discover and browse memes in a 1.5 TB collection of crawled social media. Our primary contributions include: 1) a novel technological approach and hypertext browsing design for supporting critical literacy; and 2) a highly-scalable system architecture for meme discovery, providing a solid foundation for further system extensions and refinements.
{"title":"Finding and exploring memes in social media","authors":"Hohyon Ryu, Matthew Lease, N. Woodward","doi":"10.1145/2309996.2310044","DOIUrl":"https://doi.org/10.1145/2309996.2310044","url":null,"abstract":"Online critical literacy challenges readers to recognize and question how online textual information has been shaped by its greater context. While comparing information from multiple sources provides a foundation for such awareness, keeping pace with everything being written is a daunting proposition, especially for the casual reader. We propose a new form of technological assistance for critical literacy which automatically discovers and displays underlying memes: ideas represented by similar phrases which occur across diýerent information sources. By surfacing these memes to users, we create a rich hypertext representation in which underlying memes can be explored in context. Given the vast scale of social media, we describe a highly-scalable system architecture designed for MapReduce distributed computing. To validate our approach, we report on use of our system to discover and browse memes in a 1.5 TB collection of crawled social media. Our primary contributions include: 1) a novel technological approach and hypertext browsing design for supporting critical literacy; and 2) a highly-scalable system architecture for meme discovery, providing a solid foundation for further system extensions and refinements.","PeriodicalId":91270,"journal":{"name":"HT ... : the proceedings of the ... ACM Conference on Hypertext and Social Media. ACM Conference on Hypertext and Social Media","volume":"14 1","pages":"295-304"},"PeriodicalIF":0.0,"publicationDate":"2012-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87475880","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}
Prateek Jain, P. Hitzler, Kunal Verma, P. Yeh, A. Sheth
The Linked Open Data (LOD) Cloud has gained significant traction over the past few years. With over 275 interlinked datasets across diverse domains such as life science, geography, politics, and more, the LOD Cloud has the potential to support a variety of applications ranging from open domain question answering to drug discovery. Despite its significant size (approx. 30 billion triples), the data is relatively sparely interlinked (approx. 400 million links). A semantically richer LOD Cloud is needed to fully realize its potential. Data in the LOD Cloud are currently interlinked mainly via the owl:sameAs property, which is inadequate for many applications. Additional properties capturing relations based on causality or partonomy are needed to enable the answering of complex questions and to support applications. In this paper, we present a solution to enrich the LOD Cloud by automatically detecting partonomic relationships, which are well-established, fundamental properties grounded in linguistics and philosophy. We empirically evaluate our solution across several domains, and show that our approach performs well on detecting partonomic properties between LOD Cloud data.
{"title":"Moving beyond SameAs with PLATO: partonomy detection for linked data","authors":"Prateek Jain, P. Hitzler, Kunal Verma, P. Yeh, A. Sheth","doi":"10.1145/2309996.2310004","DOIUrl":"https://doi.org/10.1145/2309996.2310004","url":null,"abstract":"The Linked Open Data (LOD) Cloud has gained significant traction over the past few years. With over 275 interlinked datasets across diverse domains such as life science, geography, politics, and more, the LOD Cloud has the potential to support a variety of applications ranging from open domain question answering to drug discovery.\u0000 Despite its significant size (approx. 30 billion triples), the data is relatively sparely interlinked (approx. 400 million links). A semantically richer LOD Cloud is needed to fully realize its potential. Data in the LOD Cloud are currently interlinked mainly via the owl:sameAs property, which is inadequate for many applications. Additional properties capturing relations based on causality or partonomy are needed to enable the answering of complex questions and to support applications.\u0000 In this paper, we present a solution to enrich the LOD Cloud by automatically detecting partonomic relationships, which are well-established, fundamental properties grounded in linguistics and philosophy. We empirically evaluate our solution across several domains, and show that our approach performs well on detecting partonomic properties between LOD Cloud data.","PeriodicalId":91270,"journal":{"name":"HT ... : the proceedings of the ... ACM Conference on Hypertext and Social Media. ACM Conference on Hypertext and Social Media","volume":"12 1","pages":"33-42"},"PeriodicalIF":0.0,"publicationDate":"2012-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82662764","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}
Shinsuke Nakajima, Jianwei Zhang, Y. Inagaki, Reyn Y. Nakamoto
In this paper, we discuss a method for early detection of "gradual buzzwords" by analyzing time-series data of blog entries. We observe the process in which certain topics grow to become major buzzwords and determine the key indicators that are necessary for their early detection. From the analysis results based on 81,922,977 blog entries from 3,776,154 blog websites posted in the past two years, we find that as topics grow to become major buzzwords, the percentages of blog entries from the blogger communities closely related to the target buzzword decrease gradually, and the percentages of blog entries from the weakly related blogger communities increase gradually. We then describe a method for early detection of these buzzwords, which is dependent on identifying the blogger communities which are closely related to these buzzwords. Moreover, we verify the effectiveness of the proposed method through experimentation that compares the rankings of several buzzword candidates with a real-life idol group popularity competition.
{"title":"Early detection of buzzwords based on large-scale time-series analysis of blog entries","authors":"Shinsuke Nakajima, Jianwei Zhang, Y. Inagaki, Reyn Y. Nakamoto","doi":"10.1145/2309996.2310042","DOIUrl":"https://doi.org/10.1145/2309996.2310042","url":null,"abstract":"In this paper, we discuss a method for early detection of \"gradual buzzwords\" by analyzing time-series data of blog entries. We observe the process in which certain topics grow to become major buzzwords and determine the key indicators that are necessary for their early detection. From the analysis results based on 81,922,977 blog entries from 3,776,154 blog websites posted in the past two years, we find that as topics grow to become major buzzwords, the percentages of blog entries from the blogger communities closely related to the target buzzword decrease gradually, and the percentages of blog entries from the weakly related blogger communities increase gradually. We then describe a method for early detection of these buzzwords, which is dependent on identifying the blogger communities which are closely related to these buzzwords. Moreover, we verify the effectiveness of the proposed method through experimentation that compares the rankings of several buzzword candidates with a real-life idol group popularity competition.","PeriodicalId":91270,"journal":{"name":"HT ... : the proceedings of the ... ACM Conference on Hypertext and Social Media. ACM Conference on Hypertext and Social Media","volume":"77 1","pages":"275-284"},"PeriodicalIF":0.0,"publicationDate":"2012-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83503812","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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