Uses of Social Media in Public Transportation

Das, Subasish; Trisha, Nusrat Fahmida; Sener, Ipek N.; Walk, Michael J.

doi:10.17226/26451

Cited by 3 publications

(2 citation statements)

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“…These platforms, accessible across devices, enable global interactions. Examples include Twitter (now known as X; the authors of this research decided to use the former platform name because the data used were generated before the renaming), Facebook, Instagram, and LinkedIn [1,2]. Users connect, share interests, and express opinions, contributing to diverse content.…”

Section: Introductionmentioning

confidence: 99%

Assessing the EU Climate and Energy Policy Priorities for Transport and Mobility through the Analysis of User-Generated Social Media Content Based on Text-Mining Techniques

Nikolaidou,

Kopsacheilis,

Gavanas

et al. 2024

Sustainability

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For over three decades, the European Union’s (EU) transport policy has aimed at fostering environmental sustainability and energy efficiency. Since 2015, European policymakers have focused more on three key sustainable development goals: decarbonizing the transport system, promoting low-emission mobility solutions, and transitioning to renewable and alternative fuels. To effectively communicate priorities and engage stakeholders, EU policymakers regularly use social media platforms like Twitter (now known as X). This active discourse involves policymakers, industrial stakeholders, the media, and the public, offering insights into the role of transport policy in addressing climate change and energy transition challenges. The current research endeavors to track and analyze the evolution of user-generated content related to climate change, energy transition, and smart mobility on Twitter from 2011 to 2021. This research uses text-mining and social network analysis techniques to quantitatively and qualitatively assess the dynamics of relevant EU policies and their effects. The study’s findings can be used to establish a robust monitoring and evaluation framework at the EU and national levels. This framework will assess the effectiveness of communicating strategic priorities for sustainable transport development. It also holds potential for application in other sectors, broadening its impact.

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Section: Introductionmentioning

confidence: 99%

Assessing the EU Climate and Energy Policy Priorities for Transport and Mobility through the Analysis of User-Generated Social Media Content Based on Text-Mining Techniques

Nikolaidou,

Kopsacheilis,

Gavanas

et al. 2024

Sustainability

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“…However, with the advent of new technologies, alternative data sources that can enhance the efficiency of transport systems are emerging. Social networking sites, in particular, are a data source that can provide valuable insights into mobility and the quality of transportation services [2], [3]. In this context, the users of these platforms can be viewed as "semantic sensors," as they have the ability to report and describe events [4].…”

Section: Introductionmentioning

confidence: 99%

Leveraging Social Media as a Source of Mobility Intelligence: An NLP-Based Approach

Fontes,

Murços,

Carneiro

et al. 2023

IEEE Open J. Intell. Transp. Syst.

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This work presents a deep learning framework for analyzing urban mobility by extracting knowledge from messages collected from Twitter. The framework, which is designed to handle largescale data and adapt automatically to new contexts, comprises three main modules: data collection and system configuration, data analytics, and aggregation and visualization. The text data is pre-processed using NLP techniques to remove informal words, slang, and misspellings. A pre-trained, unsupervised word embedding model, BERT, is used to classify travel-related tweets using a unigram approach with three dictionaries of travel-related target words: small, medium, and big. Public opinion is evaluated using VADER to classify travel-related tweets according to their sentiments. The mobility of three major cities was assessed: London, Melbourne, and New York. The framework demonstrates consistently high average performance, with a Precision of 0.80 for text classification and 0.77 for sentiment analysis. The framework can aggregate sparse information from social media and provide updated information in near real-time with high spatial resolution, enabling easy identification of traffic-related events. The framework is helpful for transportation decision-makers in operational control, tactical-strategic planning, and policy evaluation. For example, it can be used to improve the management of resources during traffic congestion or emergencies.INDEX TERMS Social media, urban mobility, text classification, sentiment analysis, framework.

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