Diamonds in the rough: Social media visual analytics for journalistic inquiry

Author(s):  
Nicholas Diakopoulos ◽  
Mor Naaman ◽  
Funda Kivran-Swaine
2016 ◽  
Vol 18 (11) ◽  
pp. 2135-2148 ◽  
Author(s):  
Yingcai Wu ◽  
Nan Cao ◽  
David Gotz ◽  
Yap-Peng Tan ◽  
Daniel A. Keim

Author(s):  
Nan-Chen Chen ◽  
Michael Brooks ◽  
Rafal Kocielnik ◽  
Sungsoo Hong ◽  
Jeff Smith ◽  
...  

i-com ◽  
2014 ◽  
Vol 13 (1) ◽  
pp. 37-44 ◽  
Author(s):  
Julia Zisgen ◽  
Julia Kern ◽  
Dennis Thom ◽  
Thomas Ertl

Zusammenfassung Anhand des Hochwassers 2013 in Deutschland soll in diesem Artikel untersucht werden, ob sich die Lageeinschätzung zur Krisenreaktion durch die Auswertung von Social Media-Daten verbessern lässt. Dabei soll insbesondere gezeigt werden, dass Techniken zur computergestützten explorativen Datenanalyse geeignet sind, um trotz der noch recht dünnen Datenlage relevante Erkenntnisse zu gewinnen. Neben einer allgemeinen Erörterung zu Nutzen und Möglichkeiten von Social Media-Daten im Bevölkerungsschutz wird dabei Scatterblogs, ein bestehendes interaktives Social Media- Analysewerkzeug, kurz vorgestellt und evaluiert. Dabei werden Daten verwendet, die während des Hochwassers aufgezeichnet wurden.


Author(s):  
Nan-Chen Chen ◽  
Michael Brooks ◽  
Rafal Kocielnik ◽  
Sungsoo (Ray) Hong ◽  
Jeff Smith ◽  
...  

10.2196/18813 ◽  
2020 ◽  
Vol 22 (7) ◽  
pp. e18813 ◽  
Author(s):  
Jie Ren ◽  
Viju Raghupathi ◽  
Wullianallur Raghupathi

Background Medical crowdfunding has emerged as a growing field for fundraising opportunities. Some environmental trends have driven the emergence of campaigns to raise funds for medical care. These trends include lack of medical insurance, economic backlash following the 2008 financial collapse, and shortcomings of health care regulations. Objective Research regarding crowdfunding campaign use, reasons, and effects on the provision of medical care and individual relationships in health systems is limited. This study aimed to explore the nature and dimensions of the phenomenon of medical crowdfunding using a visual analytics approach and data crawled from the GoFundMe crowdfunding platform in 2019. We aimed to explore and identify the factors that contribute to a successful campaign. Methods This data-driven study used a visual analytics approach. It focused on descriptive analytics to obtain a panoramic insight into medical projects funded through the GoFundMe crowdfunding platform. Results This study highlighted the relevance of positioning the campaign for fundraising. In terms of motivating donors, it appears that people are typically more generous in contributing to campaigns for children rather than those for adults. The results emphasized the differing dynamics that a picture posted in the campaign brings to the potential for medical crowdfunding. In terms of donor’s motivation, the results show that a picture depicting the pediatric patient by himself or herself is the most effective. In addition, a picture depicting the current medical condition of the patient as severe is more effective than one depicting relative normalcy in the condition. This study also drew attention to the optimum length of the title. Finally, an interesting trend in the trajectory of donations is that the average amount of a donation decreases with an increase in the number of donors. This indicates that the first donors tend to be the most generous. Conclusions This study examines the relationship between social media, the characteristics of a campaign, and the potential for fundraising. Its analysis of medical crowdfunding campaigns across the states offers a window into the status of the country’s health care affordability. This study shows the nurturing role that social media can play in the domain of medical crowdfunding. In addition, it discusses the drivers of a successful fundraising campaign with respect to the GoFundMe platform.


2021 ◽  
Author(s):  
J. Bradford Jensen ◽  
Lisa Singh ◽  
Pamela Davis-Kean ◽  
Katharine Abraham ◽  
Paul Beatty ◽  
...  

This is the fifth in a series of white papers providing a summary of the discussions and future directions that are derived from these topical meetings. This paper focuses on issues related to analysis and visual analytics. While these two topics are distinct, there are clear overlaps between the two. It is common to use different visualizations during analysis and given the sheer volume of social media data, visual analytic tools can be important during analysis, as well as during other parts of the research lifecycle. Choices about analysis may be informed by visualization plans and vice versa - both are key in communicating about a data set and what it means. We also recognized that each field of research has different analysis techniques and different levels of familiarity with visual analytics. Putting these two topics into the same meeting provided us with the opportunity to think about analysis and visual analytics/visualization in new, synergistic ways.


Author(s):  
Nicholas Diakopoulos ◽  
Mor Naaman ◽  
Tayebeh Yazdani ◽  
Funda Kivran-Swaine

2020 ◽  
Vol 23 (6) ◽  
pp. 1015-1034
Author(s):  
Kostiantyn Kucher ◽  
Rafael M. Martins ◽  
Carita Paradis ◽  
Andreas Kerren

Abstract Text visualization and visual text analytics methods have been successfully applied for various tasks related to the analysis of individual text documents and large document collections such as summarization of main topics or identification of events in discourse. Visualization of sentiments and emotions detected in textual data has also become an important topic of interest, especially with regard to the data originating from social media. Despite the growing interest in this topic, the research problem related to detecting and visualizing various stances, such as rudeness or uncertainty, has not been adequately addressed by the existing approaches. The challenges associated with this problem include the development of the underlying computational methods and visualization of the corresponding multi-label stance classification results. In this paper, we describe our work on a visual analytics platform, called StanceVis Prime, which has been designed for the analysis of sentiment and stance in temporal text data from various social media data sources. The use case scenarios intended for StanceVis Prime include social media monitoring and research in sociolinguistics. The design was motivated by the requirements of collaborating domain experts in linguistics as part of a larger research project on stance analysis. Our approach involves consuming documents from several text stream sources and applying sentiment and stance classification, resulting in multiple data series associated with source texts. StanceVis Prime provides the end users with an overview of similarities between the data series based on dynamic time warping analysis, as well as detailed visualizations of data series values. Users can also retrieve and conduct both distant and close reading of the documents corresponding to the data series. We demonstrate our approach with case studies involving political targets of interest and several social media data sources and report preliminary user feedback received from a domain expert. Graphic abstract


2017 ◽  
Vol 36 (3) ◽  
pp. 563-587 ◽  
Author(s):  
Siming Chen ◽  
Lijing Lin ◽  
Xiaoru Yuan

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