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.

scholarly journals What Your Tweets Tell Us About You: Identity, Ownership and Privacy of Twitter Data

2012 ◽  
Vol 7 (1) ◽  
pp. 174-197 ◽  
Author(s):  
Heather Small ◽  
Kristine Kasianovitz ◽  
Ronald Blanford ◽  
Ina Celaya
Keyword(s):  
Social Media ◽  
Social Networking Sites ◽  
Data Sets ◽  
Data Set ◽  
Social Media Data ◽  
Twitter Data ◽  
Other Information ◽  
Rich Data ◽  
Additional Value ◽  
Media Data

Social networking sites and other social media have enabled new forms of collaborative communication and participation for users, and created additional value as rich data sets for research. Research based on accessing, mining, and analyzing social media data has risen steadily over the last several years and is increasingly multidisciplinary; researchers from the social sciences, humanities, computer science and other domains have used social media data as the basis of their studies. The broad use of this form of data has implications for how curators address preservation, access and reuse for an audience with divergent disciplinary norms related to privacy, ownership, authenticity and reliability.In this paper, we explore how the characteristics of the Twitter platform, coupled with an ambiguous and evolving understanding of privacy in networked communication, and divergent disciplinary understandings of the resulting data, combine to create complex issues for curators trying to ensure broad-based and ethical reuse of Twitter data. We provide a case study of a specific data set to illustrate how data curators can engage with the topics and questions raised in the paper. While some initial suggestions are offered to librarians and other information professionals who are beginning to receive social media data from researchers, our larger goal is to stimulate discussion and prompt additional research on the curation and preservation of social media data.

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.

scholarly journals A Comprehensive Analysis of Approaches for Sentiment Analysis Using Twitter Data on COVID-19 Vaccines

2021 ◽  
Vol 2 (2) ◽  
pp. 1-10
Author(s):  
Amrita Mishra ◽  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Text Classification ◽  
Comprehensive Analysis ◽  
Social Media Data ◽  
The Public ◽  
Opinion Analysis ◽  
Twitter Data ◽  
Media Data

Sentiment Analysis has paved routes for opinion analysis of masses over unrestricted territorial limits. With the advent and growth of social media like Twitter, Facebook, WhatsApp, Snapchat in today’s world, stakeholders and the public often takes to expressing their opinion on them and drawing conclusions. While these social media data are extremely informative and well connected, the major challenge lies in incorporating efficient Text Classification strategies which not only overcomes the unstructured and humongous nature of data but also generates correct polarity of opinions (i.e. positive, negative, and neutral). This paper is a thorough effort to provide a brief study about various approaches to SA including Machine Learning, Lexicon Based, and Automatic Approaches. The paper also highlights the comparison of positive, negative, and neutral tweets of the Sputnik V, Moderna, and Covaxin vaccines used for preventive and emergency use of COVID-19 disease.

scholarly journals SPATIAL-TEMPORAL ANALYSIS OF SOCIAL MEDIA DATA RELATED TO NEPAL EARTHQUAKE 2015

2016 ◽  
Vol XLI-B2 ◽  
pp. 567-571
Author(s):  
L. Thapa
Keyword(s):  
Social Media ◽  
Temporal Analysis ◽  
Archaeological Sites ◽  
Social Media Data ◽  
User Input ◽  
Nepal Earthquake ◽  
Twitter Data ◽  
The Social ◽  
Media Data ◽  
Spatial Temporal Analysis

Social Medias these days have become the instant communication platform to share anything; from personal feelings to the matter of public concern, these are the easiest and aphoristic way to deliver information among the mass. With the development of Web 2.0 technologies, more and more emphasis has been given to user input in the web; the concept of Geoweb is being visualized and in the recent years, social media like Twitter, Flicker are among the popular Location Based Social Medias with locational functionality enabled in them. Nepal faced devastating earthquake on 25 April, 2015 resulting in the loss of thousands of lives, destruction in the historical-archaeological sites and properties. Instant help was offered by many countries around the globe and even lots of NGOs, INGOs and people started the rescue operations immediately; concerned authorities and people used different communication medium like Frequency Modulation Stations, Television, and Social Medias over the World Wide Web to gather information associated with the Quake and to ease the rescue activities. They also initiated campaign in the Social Media to raise the funds and support the victims. Even the social medias like Facebook, Twitter, themselves announced the helping campaign to rebuild Nepal. In such scenario, this paper features the analysis of Twitter data containing hashtag related to Nepal Earthquake 2015 together with their temporal characteristics, when were the message generated, where were these from and how these spread spatially over the internet?

scholarly journals Linking Twitter and survey data: asymmetry in quantity and its impact

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Tarek Al Baghal ◽  
Alexander Wenz ◽  
Luke Sloan ◽  
Curtis Jessop
Keyword(s):  
Social Media ◽  
Survey Data ◽  
Linked Data ◽  
Social Research ◽  
Data Sources ◽  
Social Media Data ◽  
Twitter Data ◽  
Unique Source ◽  
Methodological Aspects ◽  
Media Data

AbstractLinked social media and survey data have the potential to be a unique source of information for social research. While the potential usefulness of this methodology is widely acknowledged, very few studies have explored methodological aspects of such linkage. Respondents produce planned amounts of survey data, but highly variant amounts of social media data. This study explores this asymmetry by examining the amount of social media data available to link to surveys. The extent of variation in the amount of data collected from social media could affect the ability to derive meaningful linked indicators and could introduce possible biases. Linked Twitter data from respondents to two longitudinal surveys representative of Great Britain, the Innovation Panel and the NatCen Panel, show that there is indeed substantial variation in the number of tweets posted and the number of followers and friends respondents have. Multivariate analyses of both data sources show that only a few respondent characteristics have a statistically significant effect on the number of tweets posted, with the number of followers being the strongest predictor of posting in both panels, women posting less than men, and some evidence that people with higher education post less, but only in the Innovation Panel. We use sentiment analyses of tweets to provide an example of how the amount of Twitter data collected can impact outcomes using these linked data sources. Results show that more negatively coded tweets are related to general happiness, but not the number of positive tweets. Taken together, the findings suggest that the amount of data collected from social media which can be linked to surveys is an important factor to consider and indicate the potential for such linked data sources in social research.

scholarly journals PEIA: Personality and Emotion Integrated Attentive Model for Music Recommendation on Social Media Platforms

2020 ◽  
Vol 34 (01) ◽  
pp. 206-213
Author(s):  
Tiancheng Shen ◽  
Jia Jia ◽  
Yan Li ◽  
Yihui Ma ◽  
Yaohua Bu ◽  
...  
Keyword(s):  
Social Media ◽  
Parameter Analysis ◽  
Rapid Expansion ◽  
Music Recommendation ◽  
Short Term ◽  
Social Media Data ◽  
Social Media Platforms ◽  
Latent Representations ◽  
Media Data

With the rapid expansion of digital music formats, it's indispensable to recommend users with their favorite music. For music recommendation, users' personality and emotion greatly affect their music preference, respectively in a long-term and short-term manner, while rich social media data provides effective feedback on these information. In this paper, aiming at music recommendation on social media platforms, we propose a Personality and Emotion Integrated Attentive model (PEIA), which fully utilizes social media data to comprehensively model users' long-term taste (personality) and short-term preference (emotion). Specifically, it takes full advantage of personality-oriented user features, emotion-oriented user features and music features of multi-faceted attributes. Hierarchical attention is employed to distinguish the important factors when incorporating the latent representations of users' personality and emotion. Extensive experiments on a large real-world dataset of 171,254 users demonstrate the effectiveness of our PEIA model which achieves an NDCG of 0.5369, outperforming the state-of-the-art methods. We also perform detailed parameter analysis and feature contribution analysis, which further verify our scheme and demonstrate the significance of co-modeling of user personality and emotion in music recommendation.

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 Weather events identification in social media streams: tools to detect their evidence in Twitter

2017 ◽  
Author(s):  
Valentina Grasso ◽  
Imad Zaza ◽  
Federica Zabini ◽  
Gianni Pantaleo ◽  
Paolo Nesi ◽  
...  
Keyword(s):  
Social Media ◽  
Data Science ◽  
Social Impact ◽  
Graphical Representation ◽  
Severe Weather ◽  
Social Media Data ◽  
Twitter Data ◽  
Weather Events ◽  
Crisis Events ◽  
Media Data

Severe weather impact identification and monitoring through social media data is a good challenge for data science. In last years we assisted to an increase of natural disasters, also due to climate change. Many works showed that during such events people tend to share specific messages by of mean of social media platforms, especially Twitter. Not only they contribute to"situational" awareness also improving the dissemination of information during emergency but can be used to assess social impact of crisis events. We present in this work preliminary findings concerning how temporal distribution of weather related messages may help the identification of severe events that impacted a community. Severe weather events are recognizable by observing the synchronization of twitter streams volumes concerning extractions by using different but semantically graduate terms and hash-tags including the specific containing geo-content names. Impacting events seems immediately recognizable by graphical representation of weather streams and when the time-line show a specific parallel-wise pattern that we named "Half Onion Shape". Different but weather semantically linked twitter streams could exhibits different magnitude, in order to their term popularity, but they show, when a weather event occurs, the same temporal relative maximum. In reason of to these interesting indications, that needs to be confirmed through more deeper analysis, and of the great use of social media, as Twitter, during crisis events it's becoming fundamental to have a suite of suitable tools to monitor social media data. For Twitter data a comprehensive suite of tools is presented: the DISIT-Twitter Vigilance Platform for twitter data retrieve,management and visualization.

scholarly journals Long-term patient-reported symptoms of COVID-19: an analysis of social media data

2020 ◽  
Author(s):  
Juan M. Banda ◽  
Gurdas Viguruji Singh ◽  
Osaid Alser ◽  
DANIEL PRIETO-ALHAMBRA
Keyword(s):  
Social Media ◽  
Language Processing ◽  
Social Media Data ◽  
Persistent Symptoms ◽  
Patient Reported ◽  
Twitter Users ◽  
Initial Recovery ◽  
Clinical Records ◽  
Media Data

As the COVID-19 virus continues to infect people across the globe, there is little understanding of the long term implications for recovered patients. There have been reports of persistent symptoms after confirmed infections on patients even after three months of initial recovery. While some of these patients have documented follow-ups on clinical records, or participate in longitudinal surveys, these datasets are usually not publicly available or standardized to perform longitudinal analyses on them. Therefore, there is a need to use additional data sources for continued follow-up and identification of latent symptoms that might be underreported in other places. In this work we present a preliminary characterization of post-COVID-19 symptoms using social media data from Twitter. We use a combination of natural language processing and clinician reviews to identify long term self-reported symptoms on a set of Twitter users.

scholarly journals Optimizing Search and Data Analytics of Twitter Data using Elastic Search Algorithms

2020 ◽  
Vol 9 (6) ◽  
pp. 427-433
Keyword(s):  
Social Media ◽  
Business Intelligence ◽  
Data Analytics ◽  
Distributed Search ◽  
Time Data ◽  
Social Media Data ◽  
Twitter Data ◽  
Crucial Part ◽  
Media Data ◽  
Better Than

Quick data acquisition and analysis became an important tool in the contemporary era. Real time data is made available in World Wide Web (WWW) and social media. Especially social media data is rich in opinions of people of all walks of life. Searching and analysing such data provides required business intelligence (BI) for applications of various domains in the real world. The application may be in the area of politics or banking or insurance or healthcare industry. With the emergence of cloud computing, volumes of data are added to cloud storage infrastructure and it is growing exponentially. In this context, Elasticsearch is the distributed search and analytics engine that is very crucial part of Elastic Stack. For data collection, aggregation and enriching it Beats and Logstash are used and such data is stored in Elasticsearch. For interactive exploration and visualization Kibana is used. Elasticsearch helps in indexing of data, searching efficiently and performing data analytics. In this paper, the utility of Elasticsearch is evaluated for optimising search and data analytics of Twitter data. Empirical study is made with the Elasticsearch tool configured for Windows and also using Amazon Elasticsearch and the results are compared with state of art. The experimental results revealed that the Elasticsearch performs better than the existing ones.

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