scholarly journals A Twitter Data Credibility Framework—Hurricane Harvey as a Use Case

2019 ◽  
Vol 8 (3) ◽  
pp. 111 ◽  
Author(s):  
Jingchao Yang ◽  
Manzhu Yu ◽  
Han Qin ◽  
Mingyue Lu ◽  
Chaowei Yang
Keyword(s):  
Social Media ◽  
Situation Awareness ◽  
Text Messages ◽  
Use Case ◽  
Social Media Data ◽  
The Past ◽  
Twitter Data ◽  
Disaster Situation ◽  
Data Credibility ◽  
Media Data

Social media data have been used to improve geographic situation awareness in the past decade. Although they have free and openly availability advantages, only a small proportion is related to situation awareness, and reliability or trustworthiness is a challenge. A credibility framework is proposed for Twitter data in the context of disaster situation awareness. The framework is derived from crowdsourcing, which states that errors propagated in volunteered information decrease as the number of contributors increases. In the proposed framework, credibility is hierarchically assessed on two tweet levels. The framework was tested using Hurricane Harvey Twitter data, in which situation awareness related tweets were extracted using a set of predefined keywords including power, shelter, damage, casualty, and flood. For each tweet, text messages and associated URLs were integrated to enhance the information completeness. Events were identified by aggregating tweets based on their topics and spatiotemporal characteristics. Credibility for events was calculated and analyzed against the spatial, temporal, and social impacting scales. This framework has the potential to calculate the evolving credibility in real time, providing users insight on the most important and trustworthy events.

scholarly journals Within-subject changes in network connectivity occur during an episode of depression: evidence from a longitudinal analysis of social media posts.

2020 ◽  
Author(s):  
Sean Kelley ◽  
Claire Gillan
Keyword(s):  
Social Media ◽  
Network Theory ◽  
Depressive Episode ◽  
Network Connectivity ◽  
Depression Symptoms ◽  
Depression Severity ◽  
Social Media Data ◽  
The Past ◽  
Text Features ◽  
Media Data

Background: Network theory of mental illness posits that causal interactions between symptoms give rise to mental health disorders. This is thought to occur because positive feedback loops between symptoms trigger cascades of further symptom activation. Increasing evidence suggests that depression network connectivity is therefore a risk factor for transitioning and sustaining a depressive state. However, much of the evidence comes from cross-sectional studies that estimate networks across groups, rather than within individuals. We used a novel method to construct personalised depression-relevant networks from social media data to test the hypothesis that network connectivity is linked to depression severity and increases during a depressive episode. Methods: We analysed Twitter data from 946 participants who retrospectively reported the dates of any depressive episodes they experienced in the past 12 months and self-reported current depressive symptom severity. Daily Tweets were subjected to textual analysis, which allowed us to construct personalised, within-subject, depression networks, based on 9 a priori text features previously associated with depression severity. We tested for associations between network connectivity and current depression severity and, in participants who experienced a depressive episode in the past year, we tested if connectivity increased during the dates of a self-reported episode (N = 286). Results: Significant bivariate associations were found between current depression severity and 8/9 of the text features examined. In line with our hypothesis, individuals with greater depression severity had a significantly higher overall network connectivity between these features than those with lesser severity (β = 0.008, SE = 0.003, p = 0.002). Importantly, we observed within-subject changes in overall network connectivity associated with the dates of a self-reported depressive episode (β = 0.03, SE = 0.009, p = 0.005). Conclusions: The connectivity within personalized depression networks changes dynamically with changes in current depression symptoms. Social media data provides a fruitful, albeit noisy, source of data to test key within-subject predictions of network theory.

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.

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 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 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 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.

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

2016 ◽  
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 MapReduce and Semantics Enabled Event Detection using Social Media

2017 ◽  
Vol 7 (3) ◽  
pp. 201-213 ◽  
Author(s):  
Peng Yan
Keyword(s):  
Social Media ◽  
Event Detection ◽  
Domain Knowledge ◽  
Semantic Analysis ◽  
State Of The Art ◽  
Temporal Analysis ◽  
Semantic Interpretation ◽  
Social Media Data ◽  
Twitter Data ◽  
Media Data

Abstract Social media is playing an increasingly important role in reporting major events happening in the world. However, detecting events from social media is challenging due to the huge magnitude of the data and the complex semantics of the language being processed. This paper proposes MASEED (MapReduce and Semantics Enabled Event Detection), a novel event detection framework that effectively addresses the following problems: 1) traditional data mining paradigms cannot work for big data; 2) data preprocessing requires significant human efforts; 3) domain knowledge must be gained before the detection; 4) semantic interpretation of events is overlooked; 5) detection scenarios are limited to specific domains. In this work, we overcome these challenges by embedding semantic analysis into temporal analysis for capturing the salient aspects of social media data, and parallelizing the detection of potential events using the MapReduce methodology. We evaluate the performance of our method using real Twitter data. The results will demonstrate the proposed system outperforms most of the state-of-the-art methods in terms of accuracy and efficiency.

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