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Loneliness is detrimental to well-being and is often accompanied by self-reported feelings of not being understood by other people. What contributes to such feelings in lonely people? We used functional MRI of 66 first-year university students to unobtrusively measure the relative alignment of people’s mental processing of naturalistic stimuli and tested whether lonely people actually process the world in idiosyncratic ways. We found evidence for such idiosyncrasy: Lonely individuals’ neural responses were dissimilar to those of their peers, particularly in regions of the default-mode network in which similar responses have been associated with shared perspectives and subjective understanding. These relationships persisted when we controlled for demographic similarities, objective social isolation, and individuals’ friendships with each other. Our findings raise the possibility that being surrounded by people who see the world differently from oneself, even if one is friends with them, may be a risk factor for loneliness.
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AI-ML Analytics: A Comprehensive Investigation on Sentimental Analysis for Social Media Forensics Textual Data
Individuals spend a significant portion of their time on social media. It has become a platform for expression of feelings, sharing of ideas and connecting with other individuals using video and audio posts, textual data such as comments and descriptions and so on. Social media has a considerable impact on people’s daily life. In recent time, there is an enormous growth in number of people using Twitter and Instagram to share their emotions and sentiments which represents their actual feelings. In this work, we apply Machine Learning techniques on social media data to perform a comprehensive investigation to detect the risk of depression in people. Our work can help to detect various symptoms such sadness, loneliness, detachment etc. providing an insight for forensic analysts and law enforcement agencies about the person’s mental state. The experimental results show that Extra Tree Classifier performs significantly better over the other models in detecting the sentiment of people using social media data.
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- Award ID(s):
- 1851890
- PAR ID:
- 10500947
- Editor(s):
- Arai, K.
- Publisher / Repository:
- Springer
- Date Published:
- Journal Name:
- Lecture Notes in Networks and Systems Intelligent Computing (LNNS 739)
- ISBN:
- 978-3-031-37962-8
- Format(s):
- Medium: X
- Location:
- Switzerland, 2023
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Conference Proceeding:
- The DOI is not currently available.
