Detection of Mental Illness Risk on Social Media through Multi-level SVMs

2020 ◽  
Author(s):  
Kimia Hemmatirad ◽  
Hojjat Bagherzadeh ◽  
Ehsan Fazl-Ersi ◽  
Abedin Vahedian
Keyword(s):  
Social Media ◽  
Mental Illness ◽  
Multi Level

scholarly journals Multi-Level Relationships between Satellite-Derived Nighttime Lighting Signals and Social Media–Derived Human Population Dynamics

2018 ◽  
Vol 10 (7) ◽  
pp. 1128 ◽  
Author(s):  
Ting Ma
Keyword(s):  
Social Media ◽  
Population Dynamics ◽  
Big Data ◽  
Human Activities ◽  
Human Population ◽  
City Size ◽  
Human Settlements ◽  
Good Opportunity ◽  
Nighttime Light ◽  
Multi Level

Satellite-based measurements of the artificial nighttime light brightness (NTL) have been extensively used for studying urbanization and socioeconomic dynamics in a temporally consistent and spatially explicit manner. The increasing availability of geo-located big data detailing human population dynamics provides a good opportunity to explore the association between anthropogenic nocturnal luminosity and corresponding human activities, especially at fine time/space scales. In this study, we used Visible Infrared Imaging Radiometer Suite (VIIRS) day/night band (DNB)–derived nighttime light images and the gridded number of location requests (NLR) from China’s largest social media platform to investigate the quantitative relationship between nighttime light radiances and human population dynamics across China at four levels: the provincial, city, county, and pixel levels. Our results show that the linear relationship between the NTL and NLR might vary with the observation level and magnitude. The dispersion between the two variables likely increases with the observation scale, especially at the pixel level. The effect of spatial autocorrelation and other socioeconomic factors on the relationship should be taken into account for nighttime light-based measurements of human activities. Furthermore, the bivariate relationship between the NTL and NLR was employed to generate a partition of human settlements based on the combined features of nighttime lights and human population dynamics. Cross-regional comparisons of the partitioned results indicate a diverse co-distribution of the NTL and NLR across various types of human settlements, which could be related to the city size/form and urbanization level. Our findings may provide new insights into the multi-level responses of nighttime light signals to human activity and the potential application of nighttime light data in association with geo-located big data for investigating the spatial patterns of human settlement.

Mindfully Experimenting with IT

2014 ◽  
Vol 25 (2) ◽  
pp. 29-51 ◽  
Author(s):  
Robin S. Poston ◽  
William J. Kettinger
Keyword(s):  
Decision Making ◽  
Information Technology ◽  
Social Media ◽  
New Technology ◽  
Stakeholder Satisfaction ◽  
Media Technologies ◽  
New Information ◽  
Multi Level ◽  
Benefits And Costs

In many companies the process of new Information Technology (IT) identification and assessment lacks the rigor associated with experimentation. The realities of maintaining daily operations and the expense and expertise involved distract firms from conducting experiments. The authors explore cases of how companies introduce a new IT for the business use of digital social media. Because social media technologies are new, interest in its use is broad and diffused leading organizations to be unsure about how best to implement social media, prompting organizations to follow a mindful process of experimenting with these technologies. The cases illustrate that the extent of mindfulness influences how new technology implementations are introduced, supporting wider boundaries in assessments, richer interpretations of the IT's usefulness, multi-level foci concerning benefits and costs, persistence to continue exploration, and a greater use of fact-based decision-making. The authors observe that following a mindful introduction process reaps some of the benefits of experimentation, such as greater stakeholder satisfaction and organization-wide learning and understanding of the technology's potential.

scholarly journals PREDICTING DEPRESSION LEVEL USING SOCIAL MEDIA POSTS

2021 ◽  
Vol 8 (12) ◽  
pp. 234-237
Author(s):  
Hrishabh Patidar ◽  
Jayesh Umre
Keyword(s):  
Social Media ◽  
Mental Illness ◽  
Social Networking ◽  
Social Networking Sites ◽  
Analysis Tool ◽  
Work Study ◽  
Depression Level ◽  
Day By Day

Depression is a major concern snowballing day by day. There can be various causes of depression but mental illness is the main problem. A lot of people suffer from depression and a very few of them go through treatment. One out of six people between ages 10 to 19 years are suffering from depression. At its worst, depression can lead to suicide. Depression reduces user’s ability to do work study or socialize. One solution to this problem is study of individual’s behaviour through social media. We could know a person’s opinion, thinking, mood etc. through his social media. These attributes of user can be collected from different social networking sites like Instagram, Facebook, and Twitter etc. Social networking sites can be used as an analysis tool to predict depression level. Our projects aim is to gather information of user from their social media posts and predict his depression level.

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