Mental Health Tracker System from social media

@article{1704395,
      author = {Shruti Neeli, Sonal Raina, Ayush Razdan, Prof. P. J. Jambhulkar},
      title = {Mental Health Tracker System from social media},
      journal = {Iconic Research And Engineering Journals},
      year = {2023},
      volume = {6},
      number = {11},
      pages = {116-122},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1704395.pdf},
      abstract = {Social media platforms like Facebook, Twitter, and Instagram have brought about significant changes in our lives, connecting people like never before and creating a digital persona for individuals. While social media has several benefits, it also has some drawbacks. Recent studies have linked high usage of social media with increased levels of depression. This study focuses on using machine learning techniques to identify probable depressed Twitter users by analyzing both their network behavior and tweets. The study trains and tests classifiers to determine whether a user is depressed or not, using features extracted from their activities on the network and tweets. The findings indicate that using more features leads to higher accuracy and F-measure scores in detecting depressed users. This data-driven, predictive approach can be useful for the early detection of depression or other mental illnesses. The study's main contribution is in exploring the impact of different features on detecting depression levels. The results also highlight the challenges and limitations that machine learning researchers face in the field of mental health, and provide recommendations for future research and development in this area.},
      keywords = {Social Media Analytics, Depression, Anxiety, Machine Learning (ML), Support Vector Machine (SVM), Naive Bayes, Decision Tree, Feature Selection, Mental disease, Reddit, bipolar, ADHD.},
      month = {May},
  }