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

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.

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

10.7287/peerj.preprints.2241v2 ◽

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.

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Artificial Intelligence and Inclusion: Formerly Gang-Involved Youth as Domain Experts for Analyzing Unstructured Twitter Data

Social Science Computer Review ◽

10.1177/0894439318788314 ◽

2018 ◽

Vol 38 (1) ◽

pp. 42-56 ◽

Cited By ~ 10

Author(s):

William R. Frey ◽

Desmond U. Patton ◽

Michael B. Gaskell ◽

Kyle A. McGregor

Keyword(s):

Social Media ◽

Young People ◽

Data Science ◽

Social Impact ◽

Human Condition ◽

Social Media Data ◽

Domain Experts ◽

Marginalized Communities ◽

Twitter Data ◽

Media Data

Mining social media data for studying the human condition has created new and unique challenges. When analyzing social media data from marginalized communities, algorithms lack the ability to accurately interpret off-line context, which may lead to dangerous assumptions about and implications for marginalized communities. To combat this challenge, we hired formerly gang-involved young people as domain experts for contextualizing social media data in order to create inclusive, community-informed algorithms. Utilizing data from the Gang Intervention and Computer Science Project—a comprehensive analysis of Twitter data from gang-involved youth in Chicago—we describe the process of involving formerly gang-involved young people in developing a new part-of-speech tagger and content classifier for a prototype natural language processing system that detects aggression and loss in Twitter data. We argue that involving young people as domain experts leads to more robust understandings of context, including localized language, culture, and events. These insights could change how data scientists approach the development of corpora and algorithms that affect people in marginalized communities and who to involve in that process. We offer a contextually driven interdisciplinary approach between social work and data science that integrates domain insights into the training of qualitative annotators and the production of algorithms for positive social impact.

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

International Journal of Digital Curation ◽

10.2218/ijdc.v7i1.224 ◽

2012 ◽

Vol 7 (1) ◽

pp. 174-197 ◽

Cited By ~ 9

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.

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

Journal of Planning Education and Research ◽

10.1177/0739456x211044215 ◽

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.

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Human Behavior Analysis Using Intelligent Big Data Analytics

Frontiers in Psychology ◽

10.3389/fpsyg.2021.686610 ◽

2021 ◽

Vol 12 ◽

Author(s):

Muhammad Usman Tariq ◽

Muhammad Babar ◽

Marc Poulin ◽

Akmal Saeed Khattak ◽

Mohammad Dahman Alshehri ◽

...

Keyword(s):

Social Media ◽

Big Data ◽

Human Behavior ◽

Data Analytics ◽

Data Science ◽

Big Data Analytics ◽

Apache Spark ◽

Social Media Data ◽

The Social ◽

Media Data

Intelligent big data analysis is an evolving pattern in the age of big data science and artificial intelligence (AI). Analysis of organized data has been very successful, but analyzing human behavior using social media data becomes challenging. The social media data comprises a vast and unstructured format of data sources that can include likes, comments, tweets, shares, and views. Data analytics of social media data became a challenging task for companies, such as Dailymotion, that have billions of daily users and vast numbers of comments, likes, and views. Social media data is created in a significant amount and at a tremendous pace. There is a very high volume to store, sort, process, and carefully study the data for making possible decisions. This article proposes an architecture using a big data analytics mechanism to efficiently and logically process the huge social media datasets. The proposed architecture is composed of three layers. The main objective of the project is to demonstrate Apache Spark parallel processing and distributed framework technologies with other storage and processing mechanisms. The social media data generated from Dailymotion is used in this article to demonstrate the benefits of this architecture. The project utilized the application programming interface (API) of Dailymotion, allowing it to incorporate functions suitable to fetch and view information. The API key is generated to fetch information of public channel data in the form of text files. Hive storage machinist is utilized with Apache Spark for efficient data processing. The effectiveness of the proposed architecture is also highlighted.

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

Journal of Informatics Electrical and Electronics Engineering (JIEEE) ◽

10.54060/jieee/002.02.009 ◽

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.

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

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xli-b2-567-2016 ◽

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?

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

EPJ Data Science ◽

10.1140/epjds/s13688-021-00286-7 ◽

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.

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Analyzing social media data: A mixed-methods framework combining computational and qualitative text analysis

10.31234/osf.io/bynz4 ◽

2019 ◽

Author(s):

Matthew Andreotta ◽

Robertus Nugroho ◽

Mark Hurlstone ◽

Fabio Boschetti ◽

Simon Farrell ◽

...

Keyword(s):

Social Media ◽

Qualitative Analysis ◽

Data Science ◽

Large Data ◽

Extraction Process ◽

Data Set ◽

Diverse Range ◽

Social Media Data ◽

Qualitative Thematic Analysis ◽

Media Data

To qualitative researchers, social media offers a novel opportunity to harvest a massive and diverse range of content, without the need for intrusive or intensive data collection procedures. However, performing a qualitative analysis across a massive social media data set is cumbersome and impractical. Instead, researchers often extract a subset of content to analyze, but a framework to facilitate this process is currently lacking. We present a four-phased framework for improving this extraction process, which blends the capacities of data science techniques to compress large data sets into smaller spaces, with the capabilities of qualitative analysis to address research questions. We demonstrate this framework by investigating the topics of Australian Twitter commentary on climate change, using quantitative (Non-Negative Matrix inter-joint Factorization; Topic Alignment) and qualitative (Thematic Analysis) techniques. Our approach is useful for researchers seeking to perform qualitative analyses of social media, or researchers wanting to supplement their quantitative work with a qualitative analysis of broader social context and meaning.

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