Newcomers face various difficulties entering any communities and online forums are no exception. Due to the lack of familiarity and commitment to the group, newcomers are particularly sensitive to their early-on experiences in the forums. As a support mechanism to help newcomers blend into the group, online forums often encourage newcomers to introduce themselves upon joining the group. In this work we explored how the timing of these introduction influences newcomers' incorporation to the group. We found that providing introduction after some initial activities in the forum is associated with positive outcomes in terms of newcomers' contribution and commitment.
{"title":"Time to Introduce Myself!: Impact of Self-disclosure Timing of Newcomers in Online Discussion Forums","authors":"Di Lu, Rosta Farzan","doi":"10.1145/2786451.2786478","DOIUrl":"https://doi.org/10.1145/2786451.2786478","url":null,"abstract":"Newcomers face various difficulties entering any communities and online forums are no exception. Due to the lack of familiarity and commitment to the group, newcomers are particularly sensitive to their early-on experiences in the forums. As a support mechanism to help newcomers blend into the group, online forums often encourage newcomers to introduce themselves upon joining the group. In this work we explored how the timing of these introduction influences newcomers' incorporation to the group. We found that providing introduction after some initial activities in the forum is associated with positive outcomes in terms of newcomers' contribution and commitment.","PeriodicalId":93136,"journal":{"name":"Proceedings of the ... ACM Web Science Conference. ACM Web Science Conference","volume":"36 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86014594","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}
Schwartz (Andrew) [1] argues that inter-disciplinary approaches involving computational linguistics and the social sciences are needed to make sense of big data in social networks. The social psychology tool, the Schwartz (Shalom) Values Model [2] is used here alongside linguistic psychological attribute analysis to investigate a context in 'Twitter'. The topic of the Scottish Independence Referendum (September 18th, 2014) was selected as the context because it divided opinion into camps. This study's main hypothesis is that the camps of contexts can be values-profiled. Secondary hypotheses are: the values profiles correlate with psychological attribute profiles in the different voting camps; and the psychological textual analysis adds a wider psychological dimension to topic modeling in 'Twitter'. The methodology combined two processes: the assignment of values to the camps of the Referendum context using the Schwartz Values Model [2]; and the content analysis of the tweets, using the psychological textual analysis tool, LIWC.
{"title":"A values and psychological attribute analysis of the Scottish Independence Referendum context in Twitter","authors":"Caroline A. Halcrow, Qingpeng Zhang","doi":"10.1145/2786451.2786508","DOIUrl":"https://doi.org/10.1145/2786451.2786508","url":null,"abstract":"Schwartz (Andrew) [1] argues that inter-disciplinary approaches involving computational linguistics and the social sciences are needed to make sense of big data in social networks. The social psychology tool, the Schwartz (Shalom) Values Model [2] is used here alongside linguistic psychological attribute analysis to investigate a context in 'Twitter'. The topic of the Scottish Independence Referendum (September 18th, 2014) was selected as the context because it divided opinion into camps. This study's main hypothesis is that the camps of contexts can be values-profiled. Secondary hypotheses are: the values profiles correlate with psychological attribute profiles in the different voting camps; and the psychological textual analysis adds a wider psychological dimension to topic modeling in 'Twitter'. The methodology combined two processes: the assignment of values to the camps of the Referendum context using the Schwartz Values Model [2]; and the content analysis of the tweets, using the psychological textual analysis tool, LIWC.","PeriodicalId":93136,"journal":{"name":"Proceedings of the ... ACM Web Science Conference. ACM Web Science Conference","volume":"61 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90862488","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 we ask how regulations about commenter identity affect the quantity and quality of discussion on commenting fora. In December 2013, the Huffington Post changed the rules for its comment forums to require participants to authenticate their accounts through Facebook. This enabled a large-scale 'before and after' analysis. We collected over 42m comments on 55,000 HuffPo articles published in the period January 2013 to June 2014 and analysed them to determine how changes in identity disclosure impacted on discussions in the publication's comment pages. We first report our main results on the quantity of online commenting, where we find both a reduction and a shift in its distribution from politicised to blander topics. We then discuss the quality of discussion. Here we focus on the subset of 18.9m commenters who were active both before and after the change, in order to disentangle the effects of the worst offenders withdrawing and the remaining commenters modifying their tone. We find a 'broken windows' effect, whereby comment quality improves even when we exclude interaction with trolls and spammers.
{"title":"Anonymity and Online Commenting: The Broken Windows Effect and the End of Drive-by Commenting","authors":"R. Fredheim, Alfred Moore, J. Naughton","doi":"10.1145/2786451.2786459","DOIUrl":"https://doi.org/10.1145/2786451.2786459","url":null,"abstract":"In this study we ask how regulations about commenter identity affect the quantity and quality of discussion on commenting fora. In December 2013, the Huffington Post changed the rules for its comment forums to require participants to authenticate their accounts through Facebook. This enabled a large-scale 'before and after' analysis. We collected over 42m comments on 55,000 HuffPo articles published in the period January 2013 to June 2014 and analysed them to determine how changes in identity disclosure impacted on discussions in the publication's comment pages. We first report our main results on the quantity of online commenting, where we find both a reduction and a shift in its distribution from politicised to blander topics. We then discuss the quality of discussion. Here we focus on the subset of 18.9m commenters who were active both before and after the change, in order to disentangle the effects of the worst offenders withdrawing and the remaining commenters modifying their tone. We find a 'broken windows' effect, whereby comment quality improves even when we exclude interaction with trolls and spammers.","PeriodicalId":93136,"journal":{"name":"Proceedings of the ... ACM Web Science Conference. ACM Web Science Conference","volume":"30 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89340459","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}
Munyoung Lee, Taehoon Ha, Jinyoung Han, Jong-Youn Rha, T. Kwon
As an important part of the Internet economy, online markets have gained much interest in research community as well as industry. Researchers have studied various aspects of online markets including motivations of consumer behaviors on online markets. However, due to the lack of log data of consumers' online behaviors including their purchase, it has not been thoroughly investigated or validated on what drives consumers to purchase products on online markets. Our research moves forward from prior studies by analyzing consumers' actual online behaviors that lead to actual purchases, and using datasets from multiple online shopping sites that can provide comparisons across different types of online shopping sites. We analyzed consumers' buying process and constructed consumers' behavior trajectory to gain deeper understanding of consumer behaviors on online markets. We find that a substantial portion (24%) of consumers in a general-purpose marketplace (like eBay) discover items from external sources (e.g., price comparison sites), while most (>95%) of consumers in a special-purpose shopping site directly access items from the site itself. We also reveal that item browsing patterns and cart usage patterns are the important predictors of the actual purchases. Using behavioral features identified by our analysis, we developed a prediction model to infer whether a consumer purchases item(s). Our prediction model of purchases achieved over 80% accuracy across four different online shopping sites.
{"title":"Online Footsteps to Purchase: Exploring Consumer Behaviors on Online Shopping Sites","authors":"Munyoung Lee, Taehoon Ha, Jinyoung Han, Jong-Youn Rha, T. Kwon","doi":"10.1145/2786451.2786456","DOIUrl":"https://doi.org/10.1145/2786451.2786456","url":null,"abstract":"As an important part of the Internet economy, online markets have gained much interest in research community as well as industry. Researchers have studied various aspects of online markets including motivations of consumer behaviors on online markets. However, due to the lack of log data of consumers' online behaviors including their purchase, it has not been thoroughly investigated or validated on what drives consumers to purchase products on online markets. Our research moves forward from prior studies by analyzing consumers' actual online behaviors that lead to actual purchases, and using datasets from multiple online shopping sites that can provide comparisons across different types of online shopping sites. We analyzed consumers' buying process and constructed consumers' behavior trajectory to gain deeper understanding of consumer behaviors on online markets. We find that a substantial portion (24%) of consumers in a general-purpose marketplace (like eBay) discover items from external sources (e.g., price comparison sites), while most (>95%) of consumers in a special-purpose shopping site directly access items from the site itself. We also reveal that item browsing patterns and cart usage patterns are the important predictors of the actual purchases. Using behavioral features identified by our analysis, we developed a prediction model to infer whether a consumer purchases item(s). Our prediction model of purchases achieved over 80% accuracy across four different online shopping sites.","PeriodicalId":93136,"journal":{"name":"Proceedings of the ... ACM Web Science Conference. ACM Web Science Conference","volume":"52 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74301255","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":"Proceedings of the ACM Web Science Conference","authors":"D. D. Roure, P. Burnap, S. Halford","doi":"10.1145/2786451","DOIUrl":"https://doi.org/10.1145/2786451","url":null,"abstract":"","PeriodicalId":93136,"journal":{"name":"Proceedings of the ... ACM Web Science Conference. ACM Web Science Conference","volume":"24 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82435335","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}
Smog disasters are greatly affected by social activities such as driving. In this poster, we observe social web to enhance smog disaster forecasting. Different kinds of social indicators are measured from social web data with a social web data processing framework, and then evaluated for smog disaster forecasting with two experiments.
{"title":"Observing Social Web for Smog Disaster Forecasting","authors":"Yalin Zhou, Jiaoyan Chen, Huajun Chen","doi":"10.1145/2786451.2786454","DOIUrl":"https://doi.org/10.1145/2786451.2786454","url":null,"abstract":"Smog disasters are greatly affected by social activities such as driving. In this poster, we observe social web to enhance smog disaster forecasting. Different kinds of social indicators are measured from social web data with a social web data processing framework, and then evaluated for smog disaster forecasting with two experiments.","PeriodicalId":93136,"journal":{"name":"Proceedings of the ... ACM Web Science Conference. ACM Web Science Conference","volume":"130 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72795329","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}
An analysis of narratives in English-language weblogs reveals a unique population of individuals who post personal stories with extraordinarily high frequency over extremely long periods of time. This population includes people who have posted personal narratives everyday for more than eight years. In this paper we describe our investigation of this interesting subset of web users, where we conducted ethnographic, face-to-face interviews with a sample of these bloggers (n = 11). Our findings shed light on a culture of public documentation of private life, and provide insight into these bloggers' motivations, interactions with their readers, honesty, and thoughts on research that utilizes their data. We discuss the ethical implications for researchers working with web data, and speak to the relationship between large social media datasets and the real people behind them.
{"title":"Insights on Privacy and Ethics from the Web's Most Prolific Storytellers","authors":"C. Wienberg, A. Gordon","doi":"10.1145/2786451.2786474","DOIUrl":"https://doi.org/10.1145/2786451.2786474","url":null,"abstract":"An analysis of narratives in English-language weblogs reveals a unique population of individuals who post personal stories with extraordinarily high frequency over extremely long periods of time. This population includes people who have posted personal narratives everyday for more than eight years. In this paper we describe our investigation of this interesting subset of web users, where we conducted ethnographic, face-to-face interviews with a sample of these bloggers (n = 11). Our findings shed light on a culture of public documentation of private life, and provide insight into these bloggers' motivations, interactions with their readers, honesty, and thoughts on research that utilizes their data. We discuss the ethical implications for researchers working with web data, and speak to the relationship between large social media datasets and the real people behind them.","PeriodicalId":93136,"journal":{"name":"Proceedings of the ... ACM Web Science Conference. ACM Web Science Conference","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74345970","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 discovery of stable, heavy-tailed distributions of activity on the web has inspired many researchers to search for simple mechanisms that can cut through the complexity of countless social interactions to yield powerful new theories about human behavior. A dominant mode of investigation involves fitting a mathematical model to an observed distribution, and then inferring the behaviors that generate the modeled distribution. Yet, distributions of activity are not always stable, and the process of fitting a mathematical model to empirical distributions can be highly uncertain, especially for smaller and highly variable datasets. In this paper, we introduce an approach called skew-path analysis, which measures how concentrated information production is along different dimensions in community-generated data. The approach scales from small to large datasets, and is suitable for investigating the dynamics of online behavior. We offer a preliminary demonstration of the approach by using it to analyze six years of data from an online health community, and show that the technique offers interesting insights into the dynamics of information production. In particular, we find evidence for two distinct point attractors within a subset of the forums analyzed, demonstrating the utility of the approach.
{"title":"Taming a Menagerie of Heavy Tails with Skew Path Analysis","authors":"J. Introne, S. Goggins","doi":"10.1145/2786451.2786484","DOIUrl":"https://doi.org/10.1145/2786451.2786484","url":null,"abstract":"The discovery of stable, heavy-tailed distributions of activity on the web has inspired many researchers to search for simple mechanisms that can cut through the complexity of countless social interactions to yield powerful new theories about human behavior. A dominant mode of investigation involves fitting a mathematical model to an observed distribution, and then inferring the behaviors that generate the modeled distribution. Yet, distributions of activity are not always stable, and the process of fitting a mathematical model to empirical distributions can be highly uncertain, especially for smaller and highly variable datasets. In this paper, we introduce an approach called skew-path analysis, which measures how concentrated information production is along different dimensions in community-generated data. The approach scales from small to large datasets, and is suitable for investigating the dynamics of online behavior. We offer a preliminary demonstration of the approach by using it to analyze six years of data from an online health community, and show that the technique offers interesting insights into the dynamics of information production. In particular, we find evidence for two distinct point attractors within a subset of the forums analyzed, demonstrating the utility of the approach.","PeriodicalId":93136,"journal":{"name":"Proceedings of the ... ACM Web Science Conference. ACM Web Science Conference","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82105645","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}
Groups of people or collectives, possess a number of interesting properties even in the online world. While there are associated with positive connotations like "The Wisdom of the Crowd," not all collectives are wise. In this paper, we analyze collectives in terms of two cognitive dimensions called abstraction and expression. Based on the extent of "coagulation" of abstractions and expressions in the collective, we identify four extreme points that we call: crowds, herds, mobs and gangs respectively. We also propose and compare two computational models to score collectives along the above characterization.
{"title":"Abstractions, Expressions and Online Collectives","authors":"N. Sivaraman, S. Srinivasa","doi":"10.1145/2786451.2786499","DOIUrl":"https://doi.org/10.1145/2786451.2786499","url":null,"abstract":"Groups of people or collectives, possess a number of interesting properties even in the online world. While there are associated with positive connotations like \"The Wisdom of the Crowd,\" not all collectives are wise. In this paper, we analyze collectives in terms of two cognitive dimensions called abstraction and expression. Based on the extent of \"coagulation\" of abstractions and expressions in the collective, we identify four extreme points that we call: crowds, herds, mobs and gangs respectively. We also propose and compare two computational models to score collectives along the above characterization.","PeriodicalId":93136,"journal":{"name":"Proceedings of the ... ACM Web Science Conference. ACM Web Science Conference","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77743615","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}
Knowledge about the reception of architectural structures is crucial for architects and urban planners. Yet obtaining such information has been a challenging and costly activity. However, with the advent of the Web, a vast amount of structured and unstructured data describing architectural structures has become available publicly. This includes information about the perception and use of buildings (for instance, through social media), and structured information about the building's features and characteristics (for instance, through public Linked Data). Hence, first mining (i) the popularity of buildings from the social Web and (ii) then correlating such rankings with certain features of buildings, can provide an efficient method to identify successful architectural patterns. In this paper we propose an approach to rank buildings through the automated mining of Flickr metadata. By further correlating such rankings with building properties described in Linked Data we are able to identify popular patterns for particular building types (airports, bridges, churches, halls, and skyscrapers). Our approach combines crowdsourcing with Web mining techniques to establish influential factors, as well as ground truth to evaluate our rankings. Our extensive experimental results depict that methods tailored to specific structure types allow an accurate measurement of their public perception.
{"title":"Ranking Buildings and Mining the Web for Popular Architectural Patterns","authors":"U. Gadiraju, S. Dietze, Ernesto Diaz-Aviles","doi":"10.1145/2786451.2786467","DOIUrl":"https://doi.org/10.1145/2786451.2786467","url":null,"abstract":"Knowledge about the reception of architectural structures is crucial for architects and urban planners. Yet obtaining such information has been a challenging and costly activity. However, with the advent of the Web, a vast amount of structured and unstructured data describing architectural structures has become available publicly. This includes information about the perception and use of buildings (for instance, through social media), and structured information about the building's features and characteristics (for instance, through public Linked Data). Hence, first mining (i) the popularity of buildings from the social Web and (ii) then correlating such rankings with certain features of buildings, can provide an efficient method to identify successful architectural patterns. In this paper we propose an approach to rank buildings through the automated mining of Flickr metadata. By further correlating such rankings with building properties described in Linked Data we are able to identify popular patterns for particular building types (airports, bridges, churches, halls, and skyscrapers). Our approach combines crowdsourcing with Web mining techniques to establish influential factors, as well as ground truth to evaluate our rankings. Our extensive experimental results depict that methods tailored to specific structure types allow an accurate measurement of their public perception.","PeriodicalId":93136,"journal":{"name":"Proceedings of the ... ACM Web Science Conference. ACM Web Science Conference","volume":"128 9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89952478","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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