A Pragmatic Mixed-Methods Analysis: Identifying Perspectives and Sentiments with Social Media Data

2014 ◽  
Author(s):  
Vanessa Torres van Grinsven
Keyword(s):  
Social Media ◽  
Mixed Methods ◽  
Social Media Data ◽  
Media Data

scholarly journals Public Perspectives of Using Social Media Data to Improve Adverse Drug Reaction Reporting: A Mixed-Methods Study

Drug Safety ◽  
2021 ◽  
Vol 44 (5) ◽  
pp. 553-564 ◽  
Author(s):  
Alexander Bulcock ◽  
Lamiece Hassan ◽  
Sally Giles ◽  
Caroline Sanders ◽  
Goran Nenadic ◽  
...  
Keyword(s):  
Social Media ◽  
Adverse Drug Reaction ◽  
Drug Reaction ◽  
Mixed Methods ◽  
Adverse Drug Reaction Reporting ◽  
Mixed Methods Study ◽  
Social Media Data ◽  
Reaction Reporting ◽  
Public Perspectives ◽  
Media Data

scholarly journals Analyzing social media data: A mixed-methods framework combining computational and qualitative text analysis

2019 ◽  
Vol 51 (4) ◽  
pp. 1766-1781 ◽  
Author(s):  
Matthew Andreotta ◽  
Robertus Nugroho ◽  
Mark J. Hurlstone ◽  
Fabio Boschetti ◽  
Simon Farrell ◽  
...  
Keyword(s):  
Social Media ◽  
Mixed Methods ◽  
Text Analysis ◽  
Social Media Data ◽  
Media Data

scholarly journals Memes, scenes and #ELXN2019s: How partisans make memes during elections

2021 ◽  
pp. 146144482110206
Author(s):  
Fenwick McKelvey ◽  
Scott DeJong ◽  
Janna Frenzel
Keyword(s):  
Social Media ◽  
Mixed Methods ◽  
Political Identity ◽  
Party Identification ◽  
Political Affiliation ◽  
Social Media Data ◽  
Federal Election ◽  
Mixed Methods Approach ◽  
Facebook Pages ◽  
Media Data

Our article analyses partisan, user-generated Facebook pages and groups to understand the articulation of political identity and party identification. Adapting the concept of scenes usually found in music studies, these Facebook pages and groups act as partisan scenes that maintain identities and sentiments through participatory practices, principally by making and sharing memes. Using a mixed methods approach that combines social media data and interviews during the 2019 Canadian federal election, we find that these partisan scenes are an active part of elections and the overall political information cycle in Canada but endure beyond election cycles. Rather than trying to sway voters of different political affiliation and influence the election outcome, Facebook users employ memes to hang-out and build community, thereby reinforcing partisanship.

Smart De-Identification of Social Media Data

2014 ◽  
Author(s):  
Kathleen M. Carley ◽  
L. R. Carley ◽  
Jonathan Storrick
Keyword(s):  
Social Media ◽  
Social Media Data ◽  
Media Data

Mining Social Media Data to Study the Consequences of Dementia Diagnosis on Caregivers and Relatives (Preprint)

2018 ◽  
Author(s):  
Anika Oellrich ◽  
George Gkotsis ◽  
Richard James Butler Dobson ◽  
Tim JP Hubbard ◽  
Rina Dutta
Keyword(s):  
Social Media ◽  
Family Relationships ◽  
Text Processing ◽  
Automated Analysis ◽  
Health Concern ◽  
Dementia Diagnosis ◽  
Data Set ◽  
Social Media Data ◽  
Real Time Processing ◽  
Media Data

BACKGROUND Dementia is a growing public health concern with approximately 50 million people affected worldwide in 2017 and this number is expected to reach more than 131 million by 2050. The toll on caregivers and relatives cannot be underestimated as dementia changes family relationships, leaves people socially isolated, and affects the finances of all those involved. OBJECTIVE The aim of this study was to explore using automated analysis (i) the age and gender of people who post to the social media forum Reddit about dementia diagnoses, (ii) the affected person and their diagnosis, (iii) relevant subreddits authors are posting to, (iv) the types of messages posted and (v) the content of these posts. METHODS We analysed Reddit posts concerning dementia diagnoses. We used a previously developed text analysis pipeline to determine attributes of the posts as well as their authors to characterise online communications about dementia diagnoses. The posts were also examined by manual curation for the diagnosis provided and the person affected. Furthermore, we investigated the communities these people engage in and assessed the contents of the posts with an automated topic gathering technique. RESULTS Our results indicate that the majority of posters in our data set are women, and it is mostly close relatives such as parents and grandparents that are mentioned. Both the communities frequented and topics gathered reflect not only the sufferer's diagnosis but also potential outcomes, e.g. hardships experienced by the caregiver. The trends observed from this dataset are consistent with findings based on qualitative review, validating the robustness of social media automated text processing. CONCLUSIONS This work demonstrates the value of social media data sources as a resource for in-depth studies of those affected by a dementia diagnosis and the potential to develop novel support systems based on their real time processing in line with the increasing digitalisation of medical care.

Citizens, Elites, and Social Media Methodological Challenges and Opportunities in the Study of Persuasion and Mobilization

2019 ◽  
pp. 1036-1058
Author(s):  
Philip Habel ◽  
Yannis Theocharis
Keyword(s):  
Social Media ◽  
Big Data ◽  
Supply And Demand ◽  
Political Process ◽  
The Political ◽  
The Novel ◽  
Complete Understanding ◽  
Social Media Data ◽  
Challenges And Opportunities ◽  
Media Data

In the last decade, big data, and social media in particular, have seen increased popularity among citizens, organizations, politicians, and other elites—which in turn has created new and promising avenues for scholars studying long-standing questions of communication flows and influence. Studies of social media play a prominent role in our evolving understanding of the supply and demand sides of the political process, including the novel strategies adopted by elites to persuade and mobilize publics, as well as the ways in which citizens react, interact with elites and others, and utilize platforms to persuade audiences. While recognizing some challenges, this chapter speaks to the myriad of opportunities that social media data afford for evaluating questions of mobilization and persuasion, ultimately bringing us closer to a more complete understanding Lasswell’s (1948) famous maxim: “who, says what, in which channel, to whom, [and] with what effect.”

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