Proceedings of the 1st ACM Workshop on Social Media World Sensors最新文献

{"title":"Explaining International Migration in the Skype Network: The Role of Social Network Features","authors":"R. Kikas, M. Dumas, Ando Saabas","doi":"10.1145/2806655.2806658","DOIUrl":"https://doi.org/10.1145/2806655.2806658","url":null,"abstract":"In recent years, several new ways have appeared for quantifying human migration such as location based smartphone applications and tracking user activity from websites. To show usefulness of these new approaches, we present the results of a study of cross-country migration as observed via login events in the Skype network. We explore possibility to extract human migration and correlate it with institutional statistics. The study demonstrates that a number of social network features are strongly related to net migration from and to a given country, as well as net migration between pairs of countries. Specifically, we find that percentage of international calls, percentage of international links and foreign logins in a country, complemented by gross domestic product, can be used as relatively accurate proxies for estimating migration.","PeriodicalId":112658,"journal":{"name":"Proceedings of the 1st ACM Workshop on Social Media World Sensors","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123321564","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":"Inferring Latent Attributes of an Indian Twitter User using Celebrities and Class Influencers","authors":"Puneet Singh Ludu","doi":"10.1145/2806655.2806657","DOIUrl":"https://doi.org/10.1145/2806655.2806657","url":null,"abstract":"In this paper we classify a user into three categories: \"Gender\", \"Age\" and \"Political Affiliation\" with an application to Indian Twitter users. Our approach automatically predicts these attributes by leveraging observable information such as the tweet behavior, linguistic content of the user's Twitter feed and the celebrities followed by the user. This paper would also use a novel feature that we would define in this paper as \"class influencers\". Class influencers are the Twitter users which influence a particular class so much that, they themselves can be used as a discriminating feature. Our approach first extracts the linguistic content based features using LIWC dictionary. Then, we derive features like smiley types, smiley count, tweet frequency, night-time tweet frequency, etc. We have also derived celebrity based feature: age, genre, gender (using Wikipedia and Freebase) of the celebrities a user is following. Finally, we refine the results using class influencers. Results show that rich linguistic features combined with popular neighborhood and influencers prove valuables and promising for additional user classification needs.","PeriodicalId":112658,"journal":{"name":"Proceedings of the 1st ACM Workshop on Social Media World Sensors","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121022337","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 1st ACM Workshop on Social Media World Sensors","authors":"Mario Cataldi, Luigi Di Caro, C. Schifanella","doi":"10.1145/2806655","DOIUrl":"https://doi.org/10.1145/2806655","url":null,"abstract":"It is our great pleasure to welcome you to the 2015 ACM Workshop on Social Media World Sensors -- Sideways 2015, which is held in conjunction with the 26th ACM conference on Hypertext and Social Media -- HT 2015 in Cyprus, September 1st. \u0000 \u0000This first edition of the workshop aims at bringing together academics and practitioners from different areas to promote the vision of social media as social sensors. Nowadays, social platforms have become the most popular communication system all over the world. In fact, due to the short format of messages and the accessibility of these systems, users tend to shift from traditional communication tools (such as blogs, web sites and mailing lists) to social network for various purposes. Billions of messages are appearing daily in these services such as Twitter, Tumblr, Facebook, etc. The authors of these messages share content about their private life, exchanging opinions on a variety of topics and discussing a wide range of information news. \u0000 \u0000Even if these system cannot represent an alternative to the authoritative information media, considering the number of its users and the impressive response time of their contributions, they represent a sort of real-time news sensor that can also predate the best newspapers in informing the web community about the emerging topics and trends. In fact, the most important information media always need a certain amount of time to react to a news event; i.e. professional journalists require time, collaborators and/or technology support to provide a professional report. However a user can easily report, in few characters, what is happening in front of the user's eyes, without any concern about the readers or the writing style. These aspects make social services the most powerful sensor for events detection and automatic news generation. The aim of this workshop was to ask researchers to enter into such view, by studying how social platforms can be used in a real-time scenarios to detect emerging events and enrich them with contextual information. \u0000 \u0000The call for papers attracted submissions from India, Europe, Africa, and the United States.","PeriodicalId":112658,"journal":{"name":"Proceedings of the 1st ACM Workshop on Social Media World Sensors","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131313544","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":"Social Media Mining: Impact of the Business Model and Privacy Settings","authors":"Carsten Ellwein, Ben Noller","doi":"10.1145/2806655.2806656","DOIUrl":"https://doi.org/10.1145/2806655.2806656","url":null,"abstract":"Social media platforms have changed our lives and it is unimaginable to do without them nowadays. A large quantity of data is created that way every day; information gathered through data mining on the basis of this data is used for important decisions. The source of this social media mining has not been questioned abundantly enough in recent research. This paper represents a case study on the influence of the business model and privacy settings behind a social media platform onto the results of data mining built upon the data of these services. The social media platforms focused upon in this case are Facebook (FB) and Twitter.","PeriodicalId":112658,"journal":{"name":"Proceedings of the 1st ACM Workshop on Social Media World Sensors","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134495960","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}