32 research outputs found

    Cross-platform- and subgroup-differences in the well-being effects of Twitter, Instagram, and Facebook in the United States

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    10.1038/s41598-022-07219-yScientific Reports1213271

    Do birds of different feather flock together? Analyzing the political use of social media through a language-based approach in a multilingual context

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    This study analyzes the political use of Twitter in the run-up to the 2013 Malaysian General Election. It follows a content and social network analysis approach to investigate the interplay of language and political partisanship in social media use, among Twitter users in Malaysia. In the period leading up to the 2013 elections, Twitter posts collected under the hashtag #GE13 reveal that communities that post in English versus the Malay language, differ in how they use Twitter and with whom they interact. As compared to English users, Malay users are more likely to seek political information and express their political opinion. In online discussions, we observe language-based homophily within the English and Malay language communities, but there are some cross-cutting interactions between opposing political communities. We discuss the implications of our findings for the political use of new communication technologies in multi-ethnic and multilingual societies

    Electoral and public opinion forecasts with social media data: A meta-analysis

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    10.3390/info11040187Information (Switzerland)11418

    Estimating geographic subjective well-being from Twitter: A comparison of dictionary and data-driven language methods

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    Researchers and policy makers worldwide are interested in measuring the subjective well-being of populations. When users post on social media, they leave behind digital traces that reflect their thoughts and feelings. Aggregation of such digital traces may make it possible to monitor well-being at large scale. However, social media-based methods need to be robust to regional effects if they are to produce reliable estimates. Using a sample of 1.53 billion geotagged English tweets, we provide a systematic evaluation of word-level and data-driven methods for text analysis for generating well-being estimates for 1,208 US counties. We compared Twitter-based county-level estimates with well-being measurements provided by the Gallup-Sharecare Well-Being Index survey through 1.73 million phone surveys. We find that word-level methods (e.g., Linguistic Inquiry and Word Count [LIWC] 2015 and Language Assessment by Mechanical Turk [LabMT]) yielded inconsistent county-level well-being measurements due to regional, cultural, and socioeconomic differences in language use. However, removing as few as three of the most frequent words led to notable improvements in well-being prediction. Data-driven methods provided robust estimates, approximating the Gallup data at up to r = 0.64. We show that the findings generalized to county socioeconomic and health outcomes and were robust when poststratifying the samples to be more representative of the general US population. Regional well-being estimation from social media data seems to be robust when supervised data-driven methods are used

    Dentofacial Anomalies and Oral Hygiene Status in Mentally Challenged Children: A Survey

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    Mentally compromised patients are found to be associated with various dentofacial anomalies. These patients have physical, mental, sensory, behavioral, cognitive, emotional and chronic medical conditions, which require health rare beyond considered routine. Adequate oral cleaning in them is a task because of impaired musculature. Thus, these children are prone to various oral diseases and dentofacial anomalies that require early diagnosis and treatment. This study is carried out to know the oral hygiene status and dentofacial anomalies in mentally compromised patients with an idea of helping them to have a better oral hygiene status and treat the developed dentofacial changes on time
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