{"title":"Quantitative Analysis of Community Detection Methods for Longitudinal Mobile Data","authors":"S. Muhammad, Kristof Van Laerhoven","doi":"10.1109/SOCIETY.2013.17","DOIUrl":"https://doi.org/10.1109/SOCIETY.2013.17","url":null,"abstract":"Mobile phones are now equipped with increasingly large number of built-in sensors that can be utilized to collect long-term socio-temporal data of social interactions. Moreover, the data from different built-in sensors can be combined to predict social interactions. In this paper, we perform quantitative analysis of 6 community detection algorithms to uncover the community structure from the mobile data. We use Bluetooth, WLAN, GPS, and contact data for analysis, where each modality is modelled as an undirected weighted graph. We evaluate community detection algorithms across 6 inter-modality pairs, and use well know partition evaluation features to measure clustering similarity between the pairs. We compare the performance of different methods based on the delivered partitions, and analyse the graphs at different times to find out the community stability.","PeriodicalId":348108,"journal":{"name":"2013 International Conference on Social Intelligence and Technology","volume":"399 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132150890","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}
Nir Ofek, Cornelia Caragea, L. Rokach, P. Biyani, P. Mitra, J. Yen, K. Portier, Greta E. Greer
{"title":"Improving Sentiment Analysis in an Online Cancer Survivor Community Using Dynamic Sentiment Lexicon","authors":"Nir Ofek, Cornelia Caragea, L. Rokach, P. Biyani, P. Mitra, J. Yen, K. Portier, Greta E. Greer","doi":"10.1109/SOCIETY.2013.20","DOIUrl":"https://doi.org/10.1109/SOCIETY.2013.20","url":null,"abstract":"Online Health Communities is a major source for patients and their family members in the process of gathering information and seeking social support. The American Cancer Society Cancer Survivors Network has many users and presents a large number of users' interactions with regards to coping with cancer. Sentiment analysis is an important step in understanding participants' needs and concerns and the impact of users' responses on other members. We present an automated approach for sentiment analysis in an online cancer survivor community and compare it with a previous sentiment analysis approach. Both approaches are machine learning based and are tested on the same dataset. However, this work uses features derived from a dynamic sentiment lexicon, whereas the previous work uses a general sentiment lexicon to extract features. Tested on several classifiers, with only six features (versus thirteen), our results show 2.3% improvement on average, in terms of accuracy, and greater improvement in F-measure and AUC. An additional experiment was conducted that showed a positive impact of dimensionality reduction by extracting abstract features, instead of using term frequency (TF) vector space as attribute values.","PeriodicalId":348108,"journal":{"name":"2013 International Conference on Social Intelligence and Technology","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114342572","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}
Kristina Lerman, Prachi Jain, Rumi Ghosh, Jeon-Hyung Kang, P. Kumaraguru
{"title":"Limited Attention and Centrality in Social Networks","authors":"Kristina Lerman, Prachi Jain, Rumi Ghosh, Jeon-Hyung Kang, P. Kumaraguru","doi":"10.1109/SOCIETY.2013.11","DOIUrl":"https://doi.org/10.1109/SOCIETY.2013.11","url":null,"abstract":"How does one find important or influential people in an online social network? Researchers have proposed a variety of centrality measures to identify individuals that are, for example, often visited by a random walk, infected in an epidemic, or receive many messages from friends. Recent research suggests that a social media users' capacity to respond to an incoming message is constrained by their finite attention, which they divide over all incoming information, i.e., information sent by users they follow. We propose a new measure of centrality - limited-attention version of Bonacich's Alpha-centrality - that models the effect of limited attention on epidemic diffusion. The new measure describes a process in which nodes broadcast messages to their out-neighbors, but the neighbors' ability to receive the message depends on the number of in-neighbors they have. We evaluate the proposed measure on real-world online social networks and show that it can better reproduce an empirical influence ranking of users than other popular centrality measures.","PeriodicalId":348108,"journal":{"name":"2013 International Conference on Social Intelligence and Technology","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116383158","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}