scholarly journals Characterizing communities of hashtag usage on twitter during the 2020 COVID-19 pandemic by multi-view clustering

2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Iain J. Cruickshank ◽  
Kathleen M. Carley
Keyword(s):  
Social Media ◽  
Temporal Trends ◽  
Data Types ◽  
High Quality ◽  
Online Activity ◽  
Social Media Data ◽  
Clustering Technique ◽  
Media Data

Abstract The COVID-19 pandemic has produced a flurry of online activity on social media sites. As such, analysis of social media data during the COVID-19 pandemic can produce unique insights into discussion topics and how those topics evolve over the course of the pandemic. In this study, we propose analyzing discussion topics on Twitter by clustering hashtags. In order to obtain high-quality clusters of the Twitter hashtags, we also propose a novel multi-view clustering technique that incorporates multiple different data types that can be used to describe how users interact with hashtags. The results of our multi-view clustering show that there are distinct temporal and topical trends present within COVID-19 twitter discussion. In particular, we find that some topical clusters of hashtags shift over the course of the pandemic, while others are persistent throughout, and that there are distinct temporal trends in hashtag usage. This study is the first to use multi-view clustering to analyze hashtags and the first analysis of the greater trends of discussion occurring online during the COVID-19 pandemic.

scholarly journals Review of Data Visualization for Social Media Postings

2018 ◽  
Vol 7 (4.38) ◽  
pp. 939
Author(s):  
Nur Atiqah Sia Abdullah ◽  
Hamizah Binti Anuar
Keyword(s):  
Social Media ◽  
Data Visualization ◽  
Line Graph ◽  
Data Types ◽  
Social Media Data ◽  
Data Analyst ◽  
The Social ◽  
Social Media Platforms ◽  
Types Of Information ◽  
Media Data

Facebook and Twitter are the most popular social media platforms among netizen. People are now more aggressive to express their opinions, perceptions, and emotions through social media platforms. These massive data provide great value for the data analyst to understand patterns and emotions related to a certain issue. Mining the data needs techniques and time, therefore data visualization becomes trending in representing these types of information. This paper aims to review data visualization studies that involved data from social media postings. Past literature used node-link diagram, node-link tree, directed graph, line graph, heatmap, and stream graph to represent the data collected from the social media platforms. An analysis by comparing the social media data types, representation, and data visualization techniques is carried out based on the previous studies. This paper critically discussed the comparison and provides a suggestion for the suitability of data visualization based on the type of social media data in hand.      

scholarly journals A Secure Based Preserving Social Media Data Mangement System

2021 ◽  
Vol 10 (4) ◽  
pp. 210-214
Author(s):  
Geetha V. ◽  
Gomathy C.K. ◽  
Maddu Pavan Manikanta Kiran ◽  
Rajesh, Gandikota
Keyword(s):  
Social Media ◽  
Main Idea ◽  
Data Publishing ◽  
Activity Data ◽  
Online Activity ◽  
Social Media Data ◽  
Private Data ◽  
User Activity ◽  
Real World Datasets ◽  
Media Data

Personalized suggestions are important to help users find relevant information. It often depends on huge collection of user data, especially users’ online activity (e.g., liking/commenting/sharing) on social media, thereto user interests. Publishing such user activity makes inference attacks easy on the users, as private data (e.g., contact details) are often easily gathered from the users’ activity data. during this module, we proposed PrivacyRank, an adjustable and always protecting privacy on social media data publishing framework , which protects users against frequent attacks while giving personal ranking based recommendations. Its main idea is to continuously blur user activity data like user-specified private data is minimized under a given data budget, which matches round the ranking loss suffer from the knowledge blurring process so on preserve the usage of the info for enabling suggestions. a true world evaluation on both synthetic and real-world datasets displays that our model can provide effective and continuous protection against to the info given by the user, while still conserving the usage of the blurred data for private ranking based suggestion. Compared to other approaches, Privacy Rank achieves both better privacy protection and a far better usage altogether the rank based suggestions use cases we tested.

scholarly journals Identification Of Multifunctional Urban Activity Centers In Tokyo

2021 ◽  
Vol 14 (2) ◽  
pp. 83-91
Author(s):  
Vadim I. Boratinskii ◽  
Irina S. Tikhotskaya
Keyword(s):  
Social Media ◽  
Complex Method ◽  
Urban Structure ◽  
Distribution Analysis ◽  
Data Types ◽  
Spatial Autoregression ◽  
Social Media Data ◽  
Points Of Interest ◽  
Media Data ◽  
Urban Activity

Identification of urban activity centers is among the most important components of the urban structure study, it is necessary for reasonable planning, regulation of traffic flows and other practical measures. The purpose of this paper is to design a complex method to identify urban activity centers based on different but universal data types. In this study, we used social media data (Twitter) since it guarantees regular updates and does not rely on administrative borders and points of interest database that was considered a 'hard' representation of multifunctional urban activities. A large amount of geotagged tweets was processed by means of statistical modelling (spatial autoregression) and combined with the distribution analysis of points of interest. This allowed to identify the local centers of urban activity within 23 special wards of Tokyo more objectively and precisely than when only based on the social media data. Thereafter, delimitated centers were classified in order to define and describe their main functional and spatial characteristics. As a result of the study, railway transport was identified as the main attraction factor of the urban activity; the modern urban structure of Tokyo was identified and mapped; a new comprehensive method for identification of urban activity centers was developed and five classes of urban activity centers were defined and described.

A scholarly divide: Social media, Big Data, and unattainable scholarship

First Monday ◽  
2016 ◽  
Author(s):  
Asta Zelenkauskaite ◽  
Erik P. Bucy
Keyword(s):  
Social Media ◽  
Big Data ◽  
Predictive Accuracy ◽  
Data Types ◽  
Social Data ◽  
Social Media Data ◽  
Storage And Retrieval ◽  
Social Media Platforms ◽  
And Control ◽  
Media Data

Recent decades have witnessed an increased growth in data generated by information, communication, and technological systems, giving birth to the ‘Big Data’ paradigm. Despite the profusion of raw data being captured by social media platforms, Big Data require specialized skills to parse and analyze — and even with the requisite skills, social media data are not readily available to download. Thus, the Big Data paradigm has not produced a coincidental explosion of research opportunities for the typical scholar. The promising world of unprecedented precision and predictive accuracy that Big Data conjure remains out of reach for most communication and technology researchers, a problem that traditional platforms, namely mass media, did not present. In this paper, we evaluate the system architecture that supports the storage and retrieval of big social data, distinguishing between overt and covert data types, and how both the cost and control of social media data limit opportunities for research. Ultimately, we illuminate a curious but growing ‘scholarly divide’ between researchers with the technical know-how, funding, or institutional connections to extract big social data and the mass of researchers who merely hear big social data invoked as the latest, exciting trend in unattainable scholarship.

scholarly journals Informed consent for linking survey and social media data - Differences between platforms and data types

2021 ◽  
Vol 45 (1) ◽  
Author(s):  
Johannes Breuer ◽  
Tarek Al Baghal ◽  
Luke Sloan ◽  
Libby Bishop ◽  
Dimitra Kondyli ◽  
...  
Keyword(s):  
Social Media ◽  
Informed Consent ◽  
Data Collection ◽  
Case Studies ◽  
Survey Data ◽  
Data Types ◽  
Social Media Data ◽  
Key Aspects ◽  
Media Data ◽  
Collection Methods

Linking social media data with survey data is a way to combine the unique strengths and address some of the respective limitations of these two data types. As such linked data can be quite disclosive and potentially sensitive, it is important that researchers obtain informed consent from the individuals whose data are being linked. When formulating appropriate informed consent, there are several things that researchers need to take into account. Besides legal and ethical questions, key aspects to consider are the differences between platforms and data types. Depending on what type of social media data is collected, how the data are collected, and from which platform(s), different points need to be addressed in the informed consent. In this paper, we present three case studies in which survey data were linked with data from 1) Twitter, 2) Facebook, and 3) LinkedIn and discuss how the specific features of the platforms and data collection methods were covered in the informed consent. We compare the key attributes of these platforms that are relevant for the formulation of informed consent and also discuss scenarios of social media data collection and linking in which obtaining informed consent is not necessary. By presenting the specific case studies as well as general considerations, this paper is meant to provide guidance on informed consent for linked survey and social media data for both researchers and archivists working with this type of data.

scholarly journals Using Social Media Data to Map Mortar Shells Falling in Damascus, Syria

2021 ◽  
Vol 27 (2) ◽  
pp. 233-240
Author(s):  
Mohamad Hasan
Keyword(s):  
Social Media ◽  
Information Systems ◽  
Spatial Data ◽  
Geographical Information Systems ◽  
Hot Spot ◽  
Geographical Information ◽  
Data Types ◽  
Social Phenomena ◽  
Social Media Data ◽  
Media Data

The paper analyzes the use of social media data in geographical information systems to map the areas most affected by mortar shells in the capital of Syria, Damascus, by using geocoded and parsed social media data in geographical information systems. This paper describes a created algorithm to collecting and store data from social media sites. For the data store both a NoSQL database to save JSON format document and an RDBMS is used to save other spatial data types. A python script was written to collect the data in social media based on certain keywords related to the search. A geocoding algorithm to locate social media posts that normalize, standardize and tokenize the text was developed. The result of the developed diagram provided a year by year from 2013 to 2018 maps for mortar shell falling locations in Damascus. These layers give an overview for the changing of the numbers of mortar shells falls or in hot spot analysis for the city. Finally, social media data can prove to be useful when creating maps for dynamic social phenomena, for example, mortar shells’ location falling in Damascus, Syria. Moreover, social media data provide easy, massive, and timestamped data which makes these phenomena easier to study.

scholarly journals Measuring Americans’ Comfort With Research Uses of Their Social Media Data

2021 ◽  
Vol 7 (3) ◽  
pp. 205630512110338
Author(s):  
Sarah Gilbert ◽  
Jessica Vitak ◽  
Katie Shilton
Keyword(s):  
Social Media ◽  
Data Collection ◽  
Data Use ◽  
Data Types ◽  
Sensitive Data ◽  
Social Media Data ◽  
Content Type ◽  
Social Media Platforms ◽  
Diverse Data ◽  
Media Data

Research using online datasets from social media platforms continues to grow in prominence, but recent research suggests that platform users are sometimes uncomfortable with the ways their posts and content are used in research studies. While previous research has suggested that a variety of contextual variables may influence this discomfort, such factors have yet to be isolated and compared. In this article, we present results from a factorial vignette survey of American Facebook users. Findings reveal that researcher domain, content type, purpose of data use, and awareness of data collection all impact respondents’ comfort—measured via judgments of acceptability and concern—with diverse data uses. We provide guidance to researchers and ethics review boards about the ways that user reactions to research uses of their data can serve as a cue for identifying sensitive data types and uses.

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