The Experts below are selected from a list of 603975 Experts worldwide ranked by ideXlab platform
Francisco C Pereira - One of the best experts on this subject based on the ideXlab platform.
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competing risk mixture model and Text Analysis for sequential incident duration prediction
Transportation Research Part C-emerging Technologies, 2015Co-Authors: Francisco C Pereira, Moshe BenakivaAbstract:Predicting the duration of traffic incidents sequentially during the incident clearance period is helpful in deploying efficient measures and minimizing traffic congestion related to such incidents. This study proposes a competing risk mixture hazard-based model to analyze the effect of various factors on traffic incident duration and predict the duration sequentially. First, topic modeling, a Text Analysis technique, is used to process the Textual features of the traffic incident to extract time-dependent topics. Given four specific clearance methods and the uncertainty of these methods when used during traffic incidents, the proposed mixture model uses the multinomial logistic model and parametric hazard-based model to assess the influence of covariates on the probability of clearance methods and on the duration of the incident. Subsequently, the performance of estimated mixture model in sequentially predicting the incident duration is compared with that of the non-mixture model. The prediction results show that the presented mixture model outperforms the non-mixture model.
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Text Analysis in incident duration prediction
Transportation Research Part C-emerging Technologies, 2013Co-Authors: Francisco C Pereira, Filipe Rodrigues, Moshe EnakivaAbstract:Due to the heterogeneous case-by-case nature of traffic incidents, plenty of relevant information is recorded in free flow Text fields instead of constrained value fields. As a result, such Text components enclose considerable richness that is invaluable for incident Analysis, modeling and prediction. However, the difficulty to formally interpret such data has led to minimal consideration in previous work. In this paper, we focus on the task of incident duration prediction, more specifically on predicting clearance time, the period between incident reporting and road clearance. An accurate prediction will help traffic operators implement appropriate mitigation measures and better inform drivers about expected road blockage time. The key contribution is the introduction of topic modeling, a Text Analysis technique, as a tool for extracting information from incident reports in real time. We analyze a dataset of 2 years of accident cases and develop a machine learning based duration prediction model that integrates Textual with non-Textual features. To demonstrate the value of the approach, we compare predictions with and without Text Analysis using several different prediction models. Models using Textual features consistently outperform the others in nearly all circumstances, presenting errors up to 28% lower than models without such information.
Moshe Enakiva - One of the best experts on this subject based on the ideXlab platform.
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Text Analysis in incident duration prediction
Transportation Research Part C-emerging Technologies, 2013Co-Authors: Francisco C Pereira, Filipe Rodrigues, Moshe EnakivaAbstract:Due to the heterogeneous case-by-case nature of traffic incidents, plenty of relevant information is recorded in free flow Text fields instead of constrained value fields. As a result, such Text components enclose considerable richness that is invaluable for incident Analysis, modeling and prediction. However, the difficulty to formally interpret such data has led to minimal consideration in previous work. In this paper, we focus on the task of incident duration prediction, more specifically on predicting clearance time, the period between incident reporting and road clearance. An accurate prediction will help traffic operators implement appropriate mitigation measures and better inform drivers about expected road blockage time. The key contribution is the introduction of topic modeling, a Text Analysis technique, as a tool for extracting information from incident reports in real time. We analyze a dataset of 2 years of accident cases and develop a machine learning based duration prediction model that integrates Textual with non-Textual features. To demonstrate the value of the approach, we compare predictions with and without Text Analysis using several different prediction models. Models using Textual features consistently outperform the others in nearly all circumstances, presenting errors up to 28% lower than models without such information.
Trevor Cohen - One of the best experts on this subject based on the ideXlab platform.
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content driven Analysis of an online community for smoking cessation integration of qualitative techniques automated Text Analysis and affiliation networks
American Journal of Public Health, 2015Co-Authors: Sahiti Myneni, Kayo Fujimoto, Nathan K Cobb, Trevor CohenAbstract:Objectives. We identified content-specific patterns of network diffusion underlying smoking cessation in the conText of online platforms, with the aim of generating targeted intervention strategies.Methods. QuitNet is an online social network for smoking cessation. We analyzed 16 492 de-identified peer-to-peer messages from 1423 members, posted between March 1 and April 30, 2007. Our mixed-methods approach comprised qualitative coding, automated Text Analysis, and affiliation network Analysis to identify, visualize, and analyze content-specific communication patterns underlying smoking behavior.Results. Themes we identified in QuitNet messages included relapse, QuitNet-specific traditions, and cravings. QuitNet members who were exposed to other abstinent members by exchanging content related to interpersonal themes (e.g., social support, traditions, progress) tended to abstain. Themes found in other types of content did not show significant correlation with abstinence.Conclusions. Modeling health-related ...
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content driven Analysis of an online community for smoking cessation integration of qualitative techniques automated Text Analysis and affiliation networks
American Journal of Public Health, 2015Co-Authors: Sahiti Myneni, Kayo Fujimoto, Nathan K Cobb, Trevor CohenAbstract:OBJECTIVES: We identified content-specific patterns of network diffusion underlying smoking cessation in the conText of online platforms, with the aim of generating targeted intervention strategies. METHODS: QuitNet is an online social network for smoking cessation. We analyzed 16 492 de-identified peer-to-peer messages from 1423 members, posted between March 1 and April 30, 2007. Our mixed-methods approach comprised qualitative coding, automated Text Analysis, and affiliation network Analysis to identify, visualize, and analyze content-specific communication patterns underlying smoking behavior. RESULTS: Themes we identified in QuitNet messages included relapse, QuitNet-specific traditions, and cravings. QuitNet members who were exposed to other abstinent members by exchanging content related to interpersonal themes (e.g., social support, traditions, progress) tended to abstain. Themes found in other types of content did not show significant correlation with abstinence. CONCLUSIONS: Modeling health-related affiliation networks through content-driven methods can enable the identification of specific content related to higher abstinence rates, which facilitates targeted health promotion.
Sahiti Myneni - One of the best experts on this subject based on the ideXlab platform.
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content driven Analysis of an online community for smoking cessation integration of qualitative techniques automated Text Analysis and affiliation networks
American Journal of Public Health, 2015Co-Authors: Sahiti Myneni, Kayo Fujimoto, Nathan K Cobb, Trevor CohenAbstract:Objectives. We identified content-specific patterns of network diffusion underlying smoking cessation in the conText of online platforms, with the aim of generating targeted intervention strategies.Methods. QuitNet is an online social network for smoking cessation. We analyzed 16 492 de-identified peer-to-peer messages from 1423 members, posted between March 1 and April 30, 2007. Our mixed-methods approach comprised qualitative coding, automated Text Analysis, and affiliation network Analysis to identify, visualize, and analyze content-specific communication patterns underlying smoking behavior.Results. Themes we identified in QuitNet messages included relapse, QuitNet-specific traditions, and cravings. QuitNet members who were exposed to other abstinent members by exchanging content related to interpersonal themes (e.g., social support, traditions, progress) tended to abstain. Themes found in other types of content did not show significant correlation with abstinence.Conclusions. Modeling health-related ...
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content driven Analysis of an online community for smoking cessation integration of qualitative techniques automated Text Analysis and affiliation networks
American Journal of Public Health, 2015Co-Authors: Sahiti Myneni, Kayo Fujimoto, Nathan K Cobb, Trevor CohenAbstract:OBJECTIVES: We identified content-specific patterns of network diffusion underlying smoking cessation in the conText of online platforms, with the aim of generating targeted intervention strategies. METHODS: QuitNet is an online social network for smoking cessation. We analyzed 16 492 de-identified peer-to-peer messages from 1423 members, posted between March 1 and April 30, 2007. Our mixed-methods approach comprised qualitative coding, automated Text Analysis, and affiliation network Analysis to identify, visualize, and analyze content-specific communication patterns underlying smoking behavior. RESULTS: Themes we identified in QuitNet messages included relapse, QuitNet-specific traditions, and cravings. QuitNet members who were exposed to other abstinent members by exchanging content related to interpersonal themes (e.g., social support, traditions, progress) tended to abstain. Themes found in other types of content did not show significant correlation with abstinence. CONCLUSIONS: Modeling health-related affiliation networks through content-driven methods can enable the identification of specific content related to higher abstinence rates, which facilitates targeted health promotion.
Karina Horsti - One of the best experts on this subject based on the ideXlab platform.
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overview of nordic media research on immigration and ethnic relations from Text Analysis to the study of production use and reception
Nordicom Review, 2008Co-Authors: Karina HorstiAbstract:Nordic media and communication research had reacted to the ethnically/racially and culturally changing societies since the 1980s, and the multidisciplinary field of migration, ethnic relations and the media has been shaped. This overview draws upon existing body of research, particularly on recent literature since the early 2000s, and aims to sketch out the rough lines of Nordic media research by mapping and comparing developments in this area. In addition, it points out some major outcomes and, finally, suggests future developments. The longest line of research is based on Text Analysis, mostly quantitative and qualitative content Analysis and discourse Analysis of majority media’s Texts on immigration and ethnic minorities. Later on, the research focus has widened to cover various dimensions of media output as well as production and reception. Although the field is intensively developing, comparative research among the Nordic countries, and between other European countries,
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overview of nordic media research on immigration and ethnic relations from Text Analysis to the study of production use and reception
Social Science Research Network, 2008Co-Authors: Karina HorstiAbstract:Nordic media and communication research had reacted to the ethnically/racially and culturally changing societies since the 1980s, and the multidisciplinary field of migration, ethnic relations and the media has been shaped. This overview draws upon existing body of research, particularly on recent literature since the early 2000s, and aims to sketch out the rough lines of Nordic media research by mapping and comparing developments in this area. In addition, it points out some major outcomes and, finally, suggests future developments. The longest line of research is based on Text Analysis, mostly quantitative and qualitative content Analysis and discourse Analysis of majority media’s Texts on immigration and ethnic minorities. Later on, the research focus has widened to cover various dimensions of media output as well as production and reception. Although the field is intensively developing, comparative research among the Nordic countries, and between other European countries, is scarce.
