Opinion Formation Threshold Estimates from Different Combinations of Social Media Data-Types

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.

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

Applied Network Science ◽

10.1007/s41109-020-00317-8 ◽

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.

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Identification Of Multifunctional Urban Activity Centers In Tokyo

GEOGRAPHY ENVIRONMENT SUSTAINABILITY ◽

10.24057/2071-9388-2020-136 ◽

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.

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

First Monday ◽

10.5210/fm.v21i5.6358 ◽

2016 ◽

Cited By ~ 7

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.

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

IASSIST Quarterly ◽

10.29173/iq988 ◽

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.

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

Proceedings of the International conference “InterCarto/InterGIS” ◽

10.35595/2414-9179-2021-2-27-233-240 ◽

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.

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“Taking the Temperature of the Room”

Public Opinion Quarterly ◽

10.1093/poq/nfaa012 ◽

2020 ◽

Vol 84 (S1) ◽

pp. 236-256

Author(s):

Shannon C McGregor

Keyword(s):

Social Media ◽

Public Opinion ◽

Opinion Formation ◽

Social Media Data ◽

The Public ◽

Media System ◽

The Social ◽

New Development ◽

Use Of Social Media ◽

Media Data

Abstract For most of the twentieth century, public opinion was nearly analogous with polling. Enter social media, which has upended the social, technical, and communication contingencies upon which public opinion is constructed. This study documents how political professionals turn to social media to understand the public, charting important implications for the practice of campaigning as well as the study of public opinion itself. An analysis of in-depth interviews with 13 professionals from 2016 US presidential campaigns details how they use social media to understand and represent public opinion. I map these uses of social media onto a theoretical model, accounting for quantitative and qualitative measurement, for instrumental and symbolic purposes. Campaigns’ use of social media data to infer and symbolize public opinion is a new development in the relationship between campaigns and supporters. These new tools and symbols of public opinion are shaped by campaigns and drive press coverage (McGregor 2019), highlighting the hybrid logic of the political media system (Chadwick 2017). The model I present brings much-needed attention to qualitative data, a novel aspect of social media in understanding public opinion. The use of social media data to understand the public, for all its problems of representativeness, may provide a retort to long-standing criticisms of surveys—specifically that surveys do not reveal hierarchical, social, or public aspects of opinion formation (Blumer 1948; Herbst 1998; Cramer 2016). This model highlights a need to explicate what can—and cannot—be understood about public opinion via social media.

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

Social Media + Society ◽

10.1177/20563051211033824 ◽

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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