A System to Capture and Generation of Traffic Information from Posted Messages on Social Networks

2012 ◽  
Author(s):  
Elisa H.M. Huzita ◽  
Tainan G.F. de Souza ◽  
Yan H. Kabuki
Keyword(s):  
Social Networks ◽  
Traffic Information

scholarly journals Extraction and Analysis of Social Networks Data to Detect Traffic Accidents

Information ◽  
2022 ◽  
Vol 13 (1) ◽  
pp. 26
Author(s):  
Nestor Suat-Rojas ◽  
Camilo Gutierrez-Osorio ◽  
Cesar Pedraza
Keyword(s):  
Social Networks ◽  
Traffic Accident ◽  
Traffic Accidents ◽  
Low Cost ◽  
Information Source ◽  
Named Entity Recognition ◽  
Industrial Area ◽  
Entity Recognition ◽  
Detection Methods ◽  
Traffic Information

Traffic accident detection is an important strategy governments can use to implement policies intended to reduce accidents. They usually use techniques such as image processing, RFID devices, among others. Social network mining has emerged as a low-cost alternative. However, social networks come with several challenges such as informal language and misspellings. This paper proposes a method to extract traffic accident data from Twitter in Spanish. The method consists of four phases. The first phase establishes the data collection mechanisms. The second consists of vectorially representing the messages and classifying them as accidents or non-accidents. The third phase uses named entity recognition techniques to detect the location. In the fourth phase, locations pass through a geocoder that returns their geographic coordinates. This method was applied to Bogota city and the data on Twitter were compared with the official traffic information source; comparisons showed some influence of Twitter on the commercial and industrial area of the city. The results reveal how effective the information on accidents reported on Twitter can be. It should therefore be considered as a source of information that may complement existing detection methods.

World of the Mulio

2017 ◽  
Author(s):  
Eric E. Poehler
Keyword(s):  
Social Networks ◽  
Traffic Management ◽  
Direct Contact ◽  
Traffic Information ◽  
Archaeological Evidence ◽  
Narrative Device ◽  
The Social

Chapter 7 is the story of Sabinus, the fictional cart driver (mulio), from the Casa del Menandro. Sabinus is a narrative device invented to help imagine what the archaeological evidence cannot reveal: how traffic at Pompeii operated day-to-day and how as a system it was maintained. Although a fiction, the image of what Sabinus sees and encounters in the Pompeian street is a culmination and enlivening of the deep archaeological detail of preceding chapters. Importantly, Sabinus is also a means to explore the more speculative aspects of traffic management while simultaneously signaling the hypothetical nature of such exploration. The potential management mechanisms through which traffic information flowed are (1) the social networks that connect local drivers to civil authorities, (2) the observation of the environment and other drivers, and (3) direct contact with such information proxies as inscriptions, maps, and hired guides.

Multilayer Social Networks

2016 ◽  
Author(s):  
Mark E. Dickison ◽  
Matteo Magnani ◽  
Luca Rossi
Keyword(s):  
Social Networks ◽  
Multilayer Social Networks

Hostility and Perceptions of Support in Ambiguous Social Interactions

2006 ◽  
Vol 27 (2) ◽  
pp. 108-115 ◽  
Author(s):  
Ana-Maria Vranceanu ◽  
Linda C. Gallo ◽  
Laura M. Bogart
Keyword(s):  
Social Support ◽  
Social Networks ◽  
Information Processing ◽  
Social Interactions ◽  
Social Information Processing ◽  
Perceived Social Support ◽  
Social Information ◽  
Inverse Association ◽  
Processing Bias ◽  
Information Processing Bias

The present study investigated whether a social information processing bias contributes to the inverse association between trait hostility and perceived social support. A sample of 104 undergraduates (50 men) completed a measure of hostility and rated videotaped interactions in which a speaker disclosed a problem while a listener reacted ambiguously. Results showed that hostile persons rated listeners as less friendly and socially supportive across six conversations, although the nature of the hostility effect varied by sex, target rated, and manner in which support was assessed. Hostility and target interactively impacted ratings of support and affiliation only for men. At least in part, a social information processing bias could contribute to hostile persons' perceptions of their social networks.

Sign in / Sign up

Export Citation Format

Share Document