Social media semantic perceptions on Madrid Metro system: Using Twitter data to link complaints to space

Arjona, Joaquín Osorio; Horák, Jiří; Svoboda, Radek; García-Ruiz, Yolanda

doi:10.1016/j.scs.2020.102530

Cited by 52 publications

(44 citation statements)

References 30 publications

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“…Social network analysis allows us to quantify the connections between individual points. It helps to find patterns in the connections that sustain the society [3]. How the individual is connected or disconnected from people, groups, or populations; in other words, how the individual distributes their energy across different social groups over time or explores how an idea, belief, or disease passes through the individual's network [4].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Model for Similarity Measurement of Twitter Profiles

Shoeibi¹,

Shoeibi²,

Chamoso³

et al. 2022

Preprint

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Social media platforms have been entirely an undeniable part of the lifestyle for the past decade. Analyzing the information being shared is a crucial step to understanding human behavior. Social media analysis aims to guarantee a better experience for the user and risen user satisfaction. For deriving any further conclusion, first, it is necessary to know how to compare users. In this paper, a hybrid model has been proposed to measure Twitter profiles’ similarity and quantifies the likeness degree of profiles by calculating features considering users’ behavioral habits. For this, first, the timeline of each profile has been extracted using the official TwitterAPI. Then, in parallel, three aspects of a profile are deliberated. Behavioral ratios are time-series-related information showing the consistency and habits of the user. Dynamic time warping has been utilized to compare the behavioral ratios of two profiles. Next, the audience network is extracted for each user, and for estimating the similarity of two sets, Jaccard similarity is used. Finally, for the Content similarity measurement, the tweets are preprocessed respecting the feature extraction method; TF-IDF and DistilBERT for feature extraction are employed and then compared using the cosine similarity method. Results have shown that TF-IDF has slightly better performance; therefore, the more straightforward solution is selected for the model. Similarity level of different profiles. As in the case study, a Random Forest classification model was trained on almost 20000 users revealed a 97.24% accuracy. This comparison enables us to find duplicate profiles with nearly the same behavior and content.

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Section: Introductionmentioning

confidence: 99%

A Hybrid Model for Similarity Measurement of Twitter Profiles

Shoeibi¹,

Shoeibi²,

Chamoso³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…While there are numerous social media platforms, microblogging platforms such as Twitter are growing in popularity among people and have emerged as a useful source of data analytics (García-Palomares et al, 2017 ; Martí et al, 2019 ; El-Diraby et al, 2019 ; Osorio-Arjona et al, 2021 ; Huang, Wang, et al, 2022 ). By its very nature, micro-blogging provides a succinct and straightforward message of the content (or tweets), thereby reducing noise and allowing for a more in-depth understanding of the topics (Lansley & Longley, 2016 ; Haghighi et al, 2018 ).…”

Section: Introductionmentioning

confidence: 99%

Implications of a Twitter data-centred methodology for assessing commuters’ perceptions of the Delhi metro in India

Agrawal

Kuriakose

2022

Comput.Urban Sci.

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Owing to the onset of the new media age, the idea of e-public participation has proven to be a great complement to the limitations of the conventional public participation approach. In this respect, location-based social networks (LBSN) data can prove to be a game shift in this digital era to offer an insight into the commuter perception of service delivery. The paper aims to investigate the potential of using Twitter data to assess commuters’ perceptions of the Delhi metro, India, by presenting a comprehensive methodology for extracting, processing, and interpreting the data. The study extracts Twitter data from the official handle of the Delhi metro, performs semantic and sentiment analysis to comprehend commuters’ concerns and assesses commuters’ sentiments on the predicted concerns. The paper outlines that the current depth of Twitter data is more inclined to instantaneous responses to grievances encountered. Moreover, the analysis presents that for the data extraction period, the topics ‘Ride Safety’ and ‘Crowding’ have the lowest scores, while ‘Personnel Attitude’ and ‘Customer Interface’ have the highest scores. Further, the paper highlights insights gleaned from Twitter data in addition to the aspects included in the conventional satisfaction survey. The paper concludes by outlining the opportunities and limitations of LBSN analytics for effective public transportation decision-making in India.

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“…Compared to traditional data, SMD have clear and obvious advantages such as large data volume, rich variety and content, and fast acquisition (Krellenberg et al , 2021). Additionally, SMD are often openly available free of charge, relatively easy to capture, continuously generated and include unprecedented spatial and temporal resolution (Ghermandi and Sinclair, 2019; Osorio-Arjona et al , 2021). However, validating whether an SMD can be used as a reliable data source for visitation studies of attractions sites or open spaces is an important step in order to utilize these promising sources of information.…”

Section: Introductionmentioning

confidence: 99%

Do social media data indicate visits to tourist attractions? A case study of Shanghai, China

Liang

Zhang

2021

OHI

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PurposeCan Chinese social media data (SMD) be used as an alternative to traditional surveys used to understand tourists' visitation of attractions in Chinese cities? The purpose of this paper is to explore this question.Design/methodology/approachPopular tourism SMD sources in China, such as Ctrip, Weibo and Dazhong Dianping (DZDP), were used as data source, and the relationships between these sources and traditional data sources were studied with statistical methods. Data from Shanghai were used in this study since it is rich in tourism resources and developed in information.FindingsA systematic research method was followed and led to the following conclusions: There were positive correlations for attraction visitation between Chinese SMD and traditional survey data; Chinese SMD source could temporally indicate visits to Shanghai tourist attractions; Ctrip SMD generally performed less well than Weibo or DZDP, and different SMD performed differently depending on the specific attractions and time units in the visitation calculation process; and factors including visitation, distance from the city center and the grade of attractions might affect the prediction performance based on data from the SMD. The findings suggest that Chinese SMD could be used as a cost-efficient and reliable proxy for traditional survey data to predict Chinese attraction visitation.Originality/valueThis study applies and improves the methods of SMD reliability in attraction use studies, supplies the gap for premise, basis and foundation for the large amounts of tourism researches using SMD in China and could promote and inspire more efficient and advanced measures in tourism management and urban development.

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Social media semantic perceptions on Madrid Metro system: Using Twitter data to link complaints to space

Cited by 52 publications

References 30 publications

A Hybrid Model for Similarity Measurement of Twitter Profiles

A Hybrid Model for Similarity Measurement of Twitter Profiles

Implications of a Twitter data-centred methodology for assessing commuters’ perceptions of the Delhi metro in India

Do social media data indicate visits to tourist attractions? A case study of Shanghai, China

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