Combinatorial Approach of Feature Generation for Traffic Event Detection using Social Media Data: CFGA

Abstract

Confined use of road sensors limits the effectiveness of traffic disturbing event detection. In this context, Twitter is becoming popular among the people to share the events that affect the daily life. In this paper, a novel dictionary formation and a new feature generation approach is proposed to build an integrated machine learning framework to detect the traffic events. The proposed novel combinatorial feature generation approach (CFGA) uncovers appropriate associations among the keywords of tweet and extracts the correlated keyword sets to the data collected. Such keyword sets are denoted as set phrases . The set phrases may comprise of single or multiple words of a tweet. These set phrases may be used as keywords for event-related data collection or further analysis. The frequently occurring set phrases are identified using the notion of support, which signifies the percentage of tweets containing relevant keywords. Since the nature of different events may also vary; therefore, a hardcoded value for support thresh- old will not be beneficial. Hyper-parameter designated as support (!) is tuned for finding threshold value that is used to obtain the set phrases. This process sets up a database of frequently occurring set phrases that can signalize the traffic-related events using ML classifier. The results of the proposed approach suggest that if suitable support is chosen, proposed CFGA increases the accuracy of supervised classification models for extracting traffic information from Twitter data. The classification results obtained by using the proposed approach outperform their existing counterparts in terms of precision, recall, and F-measure.

Prospect of Threads in Contrast to Twitter as an Online Social Network Tool for Conflict Resolution

“…Other microblogging apps as explained briefly by Bhattacharya (n.d.) are Tumblr, Plurk, Twister, Gab, Reddit, Micro.blog, VK, Plerb and the like. However, Twitter is considered one of the most popular online social network services, with more than 554 million active users, competing with other popular online social network services (Chaturvedi, Toshniwa, & Parida, 2021). Even, when the Nigerian government banned Twitter in 2021, other microblogging apps could not replace Twitter as people waited till the ban was lifted and continued using the services offered by Twitter.…”

“…Twitter members can broadcast tweets and follow other users' tweets by using multiple platforms and devices (Omosotomhe & Olley, 2018). Meanwhile, some scholars asserted that "Twitter has 328 million active users every month who tweet at 230000 messages per minute" (Chaturvedi, Toshniwa, & Parida, 2021: 2). Ceron, de-Lima-Santos, & Quiles (2021: 3) thought that "…Twitter has become an indispensable social communication platform by offering many contrasting views and cultures on miscellaneous topics."…”

Investigating social media spatiotemporal transferability for transport

“…Chaniotakis et al (2016) have provided a comprehensive review of the directions that transportation-related Social Media research is positioned. In short, the directions that the literature takes are either the use of Social Media for modeling and forecasting purposes, including an aspect of the use of Social Media data for OD Estimation (Liao et al, 2021;Osorio-Arjona and García-Palomares, 2019), Attraction Models (Lee et al, 2019;Yang et al, 2018;Hu and Jin, 2018), activity modelling (Cui et al, 2018;Chaniotakis et al, 2017;Hasan and Ukkusuri, 2018;Lee et al, 2016), extraction of mobility-related and spatial characteristics (Ebrahimpour et al, 2020;Hu et al, 2020;Kim et al, 2018;Yao et al, 2018;Yang et al, 2019) transportation-related sentiment analysis (Rahman et al, 2021;Bakalos et al, 2020;Sari et al, 2019;Ali et al, 2018Ali et al, , 2019, prediction and event detection (Chaturvedi et al, 2021;Yao and Qian, 2021;Alomari et al, 2019Alomari et al, , 2021Zulfikar et al, 2019;Zhang et al, 2018;Xu et al, 2018;Pereira et al, 2015), and accessibility analysis with the complementary use of Twitter data (Kim and Lee, 2021;Qian et al, 2020;Moyano et al, 2018). On another perspective, social media have also been used mainly from transport providers, for the direct communication that their platform allow with the end users (National Academies of Sciences, Engineering, and Medicine, 2021).…”