A mobile visualization platform for exploring social media data

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Yonghong Tong ◽  
Muhammet Bakan
Keyword(s):  
Social Media ◽  
Data Visualization ◽  
Mobile Device ◽  
Large Scale ◽  
Future Research ◽  
Social Media Data ◽  
Human Behaviors ◽  
Big Picture ◽  
Analysis Platform ◽  
Media Data

With the increasing application of using mobile device and social media, large amount of continuous information about human behaviors is available. Data visualization provides an insightful presentation for the large-scale social media datasets. The focus of this paper is on the development of a mobile-device based visualization and analysis platform for social media data for the purpose of retrieving and visualizing visitors’ information for a specific region. This developed platform allows users to view the “big picture” of the visitors’ locations information. The result shows that the developed platform 1) performs a satisfied data collection and data visualization on a mobile device, 2) assists users to understand the varieties of human behaviors while visiting a place, and 3) offers a feasible role in imaging immediate information from social media and leading to further policy-making in related sectors and areas. Future research opportunities and challenges for social media data visualization are discussed.Keywords: Social media, data visualization, mobile device

scholarly journals Semantic-Aware Visual Abstraction of Large-Scale Social Media Data With Geo-Tags

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 114851-114861 ◽  
Author(s):  
Zhiguang Zhou ◽  
Xinlong Zhang ◽  
Xiaoyun Zhou ◽  
Yuhua Liu
Keyword(s):  
Social Media ◽  
Large Scale ◽  
Social Media Data ◽  
Media Data

scholarly journals Tweeting back: predicting new cases of back pain with mass social media data

2015 ◽  
Vol 23 (3) ◽  
pp. 644-648 ◽  
Author(s):  
Hopin Lee ◽  
James H McAuley ◽  
Markus Hübscher ◽  
Heidi G Allen ◽  
Steven J Kamper ◽  
...  
Keyword(s):  
Risk Factors ◽  
Social Media ◽  
Back Pain ◽  
Health Interventions ◽  
Future Research ◽  
Preventive Interventions ◽  
Public Health Interventions ◽  
Social Media Data ◽  
Media Data ◽  
Global Health Problem

Background Back pain is a global health problem. Recent research has shown that risk factors that are proximal to the onset of back pain might be important targets for preventive interventions. Rapid communication through social media might be useful for delivering timely interventions that target proximal risk factors. Identifying individuals who are likely to discuss back pain on Twitter could provide useful information to guide online interventions. Methods We used a case-crossover study design for a sample of 742 028 tweets about back pain to quantify the risks associated with a new tweet about back pain. Results The odds of tweeting about back pain just after tweeting about selected physical, psychological, and general health factors were 1.83 (95% confidence interval [CI], 1.80-1.85), 1.85 (95% CI: 1.83-1.88), and 1.29 (95% CI, 1.27-1.30), respectively. Conclusion These findings give directions for future research that could use social media for innovative public health interventions.

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.

Fad or Here to Stay: Predicting Product Market Adoption and Longevity Using Large Scale, Social Media Data

2013 ◽  
Author(s):  
Suppawong Tuarob ◽  
Conrad S. Tucker
Keyword(s):  
Social Media ◽  
Knowledge Discovery ◽  
Large Scale ◽  
Product Information ◽  
Product Market ◽  
Knowledge Discovery In Databases ◽  
Product Reviews ◽  
Product Model ◽  
Social Media Data ◽  
Media Data

The authors of this work propose a Knowledge Discovery in Databases (KDD) model for predicting product market adoption and longevity using large scale, social media data. Social media data, available through sites such as Twitter® and Facebook®, have been shown to be leading indicators and predictors of events ranging from influenza spread, financial stock market prices, and movie revenues. Being ubiquitous and colloquial in nature allows users to honestly express their opinions in a unified, dynamic manner. This makes social media a relatively new data gathering source that can potentially appeal to designers and enterprise decision makers aiming to understand consumers response to their upcoming/newly launched products. Existing design methodologies for leveraging large scale data have traditionally relied on product reviews available on the internet to mine product information. However, such web reviews often come from disparate sources, making the aggregation and knowledge discovery process quite cumbersome, especially reviews for poorly received products. Furthermore, such web reviews have not been shown to be strong indicators of new product market adoption. In this paper, the authors demonstrate how social media can be used to predict and mine information relating to product features, product competition and market adoption. In particular, the authors analyze the sentiment in tweets and use the results to predict product sales. The authors present a mathematical model that can quantify the correlations between social media sentiment and product market adoption in an effort to compute the ability to stay in the market of individual products. The proposed technique involves computing the Subjectivity, Polarity, and Favorability of the product. Finally, the authors utilize Information Retrieval techniques to mine users’ opinions about strong, weak, and controversial features of a given product model. The authors evaluate their approaches using the real-world smartphone data, which are obtained from www.statista.com and www.gsmarena.com.

scholarly journals Reuters tracer: Toward automated news production using large scale social media data

2017 ◽  
Author(s):  
Xiaomo Liu ◽  
Armineh Nourbakhsh ◽  
Quanzhi Li ◽  
Sameena Shah ◽  
Robert Martin ◽  
...  
Keyword(s):  
Social Media ◽  
Large Scale ◽  
News Production ◽  
Social Media Data ◽  
Media Data

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.      

Visual abstraction and exploration of large-scale geographical social media data

2020 ◽  
Vol 376 ◽  
pp. 244-255 ◽  
Author(s):  
Zhiguang Zhou ◽  
Xinlong Zhang ◽  
Zhiyong Guo ◽  
Yuhua Liu
Keyword(s):  
Social Media ◽  
Large Scale ◽  
Social Media Data ◽  
Media Data

Automated Discovery of Lead Users and Latent Product Features by Mining Large Scale Social Media Networks

2015 ◽  
Vol 137 (7) ◽  
Author(s):  
Suppawong Tuarob ◽  
Conrad S. Tucker
Keyword(s):  
Social Media ◽  
Large Scale ◽  
User Preferences ◽  
Social Media Data ◽  
Social Media Networks ◽  
Lead User ◽  
Product Features ◽  
Latent Features ◽  
Lead Users ◽  
Media Data

Lead users play a vital role in next generation product development, as they help designers discover relevant product feature preferences months or even years before they are desired by the general customer base. Existing design methodologies proposed to extract lead user preferences are typically constrained by temporal, geographic, size, and heterogeneity limitations. To mitigate these challenges, the authors of this work propose a set of mathematical models that mine social media networks for lead users and the product features that they express relating to specific products. The authors hypothesize that: (i) lead users are discoverable from large scale social media networks and (ii) product feature preferences, mined from lead user social media data, represent product features that do not currently exist in product offerings but will be desired in future product launches. An automated approach to lead user product feature identification is proposed to identify latent features (product features unknown to the public) from social media data. These latent features then serve as the key to discovering innovative users from the ever increasing pool of social media users. The authors collect 2.1 × 109 social media messages in the United States during a period of 31 months (from March 2011 to September 2013) in order to determine whether lead user preferences are discoverable and relevant to next generation cell phone designs.

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