scholarly journals Identifying Public Perceptions toward Emerging Transportation Trends through Social Media-Based Interactions

2021 ◽  
Vol 1 (3) ◽  
pp. 794-813
Author(s):  
Md Rakibul Alam ◽  
Arif Mohaimin Sadri ◽  
Xia Jin
Keyword(s):  
Social Media ◽  
Built Environment ◽  
Large Scale ◽  
User Fees ◽  
Data Driven ◽  
Social Media Data ◽  
Shared Mobility ◽  
Vehicle Technology ◽  
Spatio Temporal ◽  
Media Data

The objective of this study is to mine and analyze large-scale social media data (rich spatio-temporal data unlike traditional surveys) and develop comparative infographics of emerging transportation trends and mobility indicators by adopting natural language processing and data-driven techniques. As such, first, around 13 million tweets for about 20 days (16 December 2019–4 January 2020) from North America were collected, and tweets closely aligned with emerging transportation and mobility trends (such as shared mobility, vehicle technology, built environment, user fees, telecommuting, and e-commerce) were identified. Data analytics captured spatio-temporal differences in social media user interactions and concerns about such trends, as well as topics of discussions formed through such interactions. California, Florida, Georgia, Illinois, New York are among the highly visible cities discussing such trends. Being positive overall, people carried more positive views on shared mobility, vehicle technology, telecommuting, and e-commerce, while being more negative on user fees, and the built environment. Ride-hailing, fuel efficiency, trip navigation, daily as well as shopping and recreational activities, gas price, tax, and product delivery were among the emergent topics. The social media data-driven framework would allow real-time monitoring of transportation trends by agencies, researchers, and professionals.

scholarly journals Real-time spatio-temporal event detection on geotagged social media

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Yasmeen George ◽  
Shanika Karunasekera ◽  
Aaron Harwood ◽  
Kwan Hui Lim
Keyword(s):  
New York ◽  
Social Media ◽  
Event Detection ◽  
Detection System ◽  
Time And Space ◽  
Social Media Data ◽  
Event Time ◽  
Spatio Temporal ◽  
Geographical Space ◽  
Media Data

AbstractA key challenge in mining social media data streams is to identify events which are actively discussed by a group of people in a specific local or global area. Such events are useful for early warning for accident, protest, election or breaking news. However, neither the list of events nor the resolution of both event time and space is fixed or known beforehand. In this work, we propose an online spatio-temporal event detection system using social media that is able to detect events at different time and space resolutions. First, to address the challenge related to the unknown spatial resolution of events, a quad-tree method is exploited in order to split the geographical space into multiscale regions based on the density of social media data. Then, a statistical unsupervised approach is performed that involves Poisson distribution and a smoothing method for highlighting regions with unexpected density of social posts. Further, event duration is precisely estimated by merging events happening in the same region at consecutive time intervals. A post processing stage is introduced to filter out events that are spam, fake or wrong. Finally, we incorporate simple semantics by using social media entities to assess the integrity, and accuracy of detected events. The proposed method is evaluated using different social media datasets: Twitter and Flickr for different cities: Melbourne, London, Paris and New York. To verify the effectiveness of the proposed method, we compare our results with two baseline algorithms based on fixed split of geographical space and clustering method. For performance evaluation, we manually compute recall and precision. We also propose a new quality measure named strength index, which automatically measures how accurate the reported event is.

scholarly journals Semantic-Aware Visual Abstraction of Large-Scale Social Media Data With Geo-Tags

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 114851-114861 ◽  
Author(s):  
Zhiguang Zhou ◽  
Xinlong Zhang ◽  
Xiaoyun Zhou ◽  
Yuhua Liu
Keyword(s):  
Social Media ◽  
Large Scale ◽  
Social Media Data ◽  
Media Data

Changing Trends in Long-Term Sentiments and Neighborhood Determinants in a Shrinking City

2021 ◽  
pp. 0739456X2110442
Author(s):  
Yunmi Park ◽  
Minju Kim ◽  
Jiyeon Shin ◽  
Megan E. Heim LaFrombois
Keyword(s):  
Social Media ◽  
Built Environment ◽  
Urban Regeneration ◽  
Neighborhood Conditions ◽  
Social Media Data ◽  
Twitter Data ◽  
Changing Trends ◽  
Shrinking City ◽  
Media Data

This research examined social media’s role in understanding perceptions about the spaces in which individuals interact, what planners can learn from social media data, and how to use social media to inform urban regeneration efforts. Using Twitter data from 2010 to 2018 recorded in one U.S. shrinking city, Detroit, Michigan, this paper longitudinally investigated topics that people discuss, their emotions, and neighborhood conditions associated with these topics and sentiments. Findings demonstrate that neighborhood demographics, socioeconomic, and built environment conditions impact people’s sentiments.

Who’s Tweeting About the President? What Big Survey Data Can Tell Us About Digital Traces?

2019 ◽  
Vol 38 (5) ◽  
pp. 633-650 ◽  
Author(s):  
Josh Pasek ◽  
Colleen A. McClain ◽  
Frank Newport ◽  
Stephanie Marken
Keyword(s):  
Social Media ◽  
Survey Data ◽  
Large Scale ◽  
Presidential Approval ◽  
Social Phenomena ◽  
Social Media Data ◽  
Complex Picture ◽  
Large Scale Survey ◽  
Media Data

Researchers hoping to make inferences about social phenomena using social media data need to answer two critical questions: What is it that a given social media metric tells us? And who does it tell us about? Drawing from prior work on these questions, we examine whether Twitter sentiment about Barack Obama tells us about Americans’ attitudes toward the president, the attitudes of particular subsets of individuals, or something else entirely. Specifically, using large-scale survey data, this study assesses how patterns of approval among population subgroups compare to tweets about the president. The findings paint a complex picture of the utility of digital traces. Although attention to subgroups improves the extent to which survey and Twitter data can yield similar conclusions, the results also indicate that sentiment surrounding tweets about the president is no proxy for presidential approval. Instead, after adjusting for demographics, these two metrics tell similar macroscale, long-term stories about presidential approval but very different stories at a more granular level and over shorter time periods.

Fad or Here to Stay: Predicting Product Market Adoption and Longevity Using Large Scale, Social Media Data

2013 ◽  
Author(s):  
Suppawong Tuarob ◽  
Conrad S. Tucker
Keyword(s):  
Social Media ◽  
Knowledge Discovery ◽  
Large Scale ◽  
Product Information ◽  
Product Market ◽  
Knowledge Discovery In Databases ◽  
Product Reviews ◽  
Product Model ◽  
Social Media Data ◽  
Media Data

The authors of this work propose a Knowledge Discovery in Databases (KDD) model for predicting product market adoption and longevity using large scale, social media data. Social media data, available through sites such as Twitter® and Facebook®, have been shown to be leading indicators and predictors of events ranging from influenza spread, financial stock market prices, and movie revenues. Being ubiquitous and colloquial in nature allows users to honestly express their opinions in a unified, dynamic manner. This makes social media a relatively new data gathering source that can potentially appeal to designers and enterprise decision makers aiming to understand consumers response to their upcoming/newly launched products. Existing design methodologies for leveraging large scale data have traditionally relied on product reviews available on the internet to mine product information. However, such web reviews often come from disparate sources, making the aggregation and knowledge discovery process quite cumbersome, especially reviews for poorly received products. Furthermore, such web reviews have not been shown to be strong indicators of new product market adoption. In this paper, the authors demonstrate how social media can be used to predict and mine information relating to product features, product competition and market adoption. In particular, the authors analyze the sentiment in tweets and use the results to predict product sales. The authors present a mathematical model that can quantify the correlations between social media sentiment and product market adoption in an effort to compute the ability to stay in the market of individual products. The proposed technique involves computing the Subjectivity, Polarity, and Favorability of the product. Finally, the authors utilize Information Retrieval techniques to mine users’ opinions about strong, weak, and controversial features of a given product model. The authors evaluate their approaches using the real-world smartphone data, which are obtained from www.statista.com and www.gsmarena.com.

scholarly journals Reuters tracer: Toward automated news production using large scale social media data

2017 ◽  
Author(s):  
Xiaomo Liu ◽  
Armineh Nourbakhsh ◽  
Quanzhi Li ◽  
Sameena Shah ◽  
Robert Martin ◽  
...  
Keyword(s):  
Social Media ◽  
Large Scale ◽  
News Production ◽  
Social Media Data ◽  
Media Data

Visual abstraction and exploration of large-scale geographical social media data

2020 ◽  
Vol 376 ◽  
pp. 244-255 ◽  
Author(s):  
Zhiguang Zhou ◽  
Xinlong Zhang ◽  
Zhiyong Guo ◽  
Yuhua Liu
Keyword(s):  
Social Media ◽  
Large Scale ◽  
Social Media Data ◽  
Media Data

The Role of Smart Technologies to Support Citizen Engagement and Decision Making

2018 ◽  
Vol 14 (4) ◽  
pp. 1-17 ◽  
Author(s):  
Gabriela Viale Pereira ◽  
Gregor Eibl ◽  
Constantinos Stylianou ◽  
Gilberto Martínez ◽  
Haris Neophytou ◽  
...  
Keyword(s):  
Decision Making ◽  
Social Media ◽  
Real Life ◽  
Digital Technologies ◽  
Fuzzy Cognitive Maps ◽  
Data Driven ◽  
Social Media Data ◽  
Life Problems ◽  
Smart Technologies ◽  
Media Data

Smart government relies both on the application of digital technologies to enable citizen's participation in order to achieve a high level of citizen centricity and on data-driven decision making in order to improve the quality of life of citizens. Data-driven decisions in turn depend on accessible and reliable datasets, which open government and social media data are likely to promise. The SmartGov project uses digital technologies by integrating open and social media data in Fuzzy Cognitive Maps to model real life problems and simulate different scenarios leading to better decision making. This research performed a multiple-case analysis in two pilot cities. Both municipalities use the technologies to find the best routes: Limassol to improve the garbage collection and Quart de Poblet to improve the walking routes of chaperones guiding children to school. The article proposes a generic framework for Smart City Governance focusing on the inputs and outcomes of this process in the use of technologies for policy making built based on the analysis of the SmartGov.

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