Personalized travel route recommendation from multi-source social 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.

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

The Oxford Handbook of Electoral Persuasion ◽

10.1093/oxfordhb/9780190860806.013.27 ◽

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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Emergency flood detection using multiple information sources: Integrated analysis of natural hazard monitoring and social media data

The Science of The Total Environment ◽

10.1016/j.scitotenv.2020.144371 ◽

2020 ◽

pp. 144371

Author(s):

Kikuko Shoyama ◽

Qinglin Cui ◽

Makoto Hanashima ◽

Hiroaki Sano ◽

Yuichiro Usuda

Keyword(s):

Social Media ◽

Information Sources ◽

Natural Hazard ◽

Integrated Analysis ◽

Social Media Data ◽

Flood Detection ◽

Media Data

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Missing value or behaviour: how to increase the signal of social media data

METRON ◽

10.1007/s40300-021-00216-7 ◽

2021 ◽

Author(s):

Paolo Mariani ◽

Andrea Marletta

Keyword(s):

Social Media ◽

Missing Data ◽

Everyday Life ◽

Processing Technique ◽

Missing Value ◽

Social Media Data ◽

Practical Strategy ◽

Specific Behaviour ◽

Complex Features ◽

Media Data

AbstractSocial media has become a widespread element of people’s everyday life, which is used to communicate and generate contents. Among the several ways to express a reaction to social media contents, the “Likes” are critical. Indeed, they convey preferences, which drive existing markets or allow the creation of new ones. Nevertheless, the appreciation indicators have some complex features, as for example the interpretation of the absence of “Likes”. In this case, the lack of approval may be considered as a specific behaviour. The present study aimed to define whether the absence of Likes may indicate the presence of a specific behaviour through the contextualization of the treatment of missing data applied to real cases. We provided a practical strategy for extracting more knowledge from social media data, whose synthesis raises several measurement problems. We proposed an approach based on the disambiguation of missing data in two modalities: “Dislike” and “Nothing”. Finally, a data pre-processing technique was suggested to increase the signal of social media data.

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