Learning, Mental Health, and Adverse Drug Reactions on Social Media edit
Applications of Twitter and Other Social Media edit
Twitter offers a massive platform on which many subjects and topics can be discussed and shared. This massive platform can be accessed by anyone and is an enormous resource for all sorts of applications.
Some general applications of Twitter include:
- Natural disasters bring large amounts of destruction, and tweets by Twitter users can make it easier to identify where relief is needed. [1]
- The political atmosphere is often seen as unpredictable, but by measuring the mood on Twitter, researchers have been able to predict elections. [2]
- Economics can seem daunting and intimidating. Twitter data has also been proven useful in predicting how the stock market will shift and shape from day to day. [3]
- Mental health and adverse drug reactions are both serious causes of death, especially in the United States. Twitter has been proven to be able to predict and determine if a user is suffering from one. [4][5]
- Twitter has been used to determine if a user can effectively learn about a new topic from tweets. [6]
Twitter and Learning edit
Informal learning can be defined as any learning done in a non-classroom setting that is unplanned or spontaneous. Formal learning is done in a formal setting, like a classroom. Informal learning takes place in everyday circumstances, and most people do not notice it happening. Throughout a day, an individual's brain takes in a massive amount of information and processes it. The act of processing this information is informal learning. Through interactions with people, reading materials, conversations, as well as the utilizing the five senses, an individual learns about people, places, and things everyday. This process can be seen happening via Twitter.[6]Because Twitter's platform is a microblog, anyone can post anything that they choose. When tweets of a certain subject or topic are gathered, there is a large amount of new information available to the user. For example, during the Occupy Wall Street movement, there were large amounts of tweets regarding the movement, the protesters, and the critics of the protesters. By utilizing this data from various sources like, news agencies, bystanders, critics, and the protesters themselves, a user is able to piece together what was happening, why people were protesting, and why some disagreed. This is a prime example of informal learning, not only because the user gains knowledge they lacked previously, but also they are able to form an opinion and discuss the subject with others. While Twitter and other social media platforms offer this knowledge, the user must have the sense to determine which sources are credible, what information is true, and also be able to sift through data on such a large scale without losing interest.
Diagnosing Mental Health through Twitter edit
Mental health data is highly prevalent on social media, with thousands of tweets or posts being sent everyday. These tweets include tweets about all kinds of mental health disorders, treatment options, and fighting the stigma that comes with having a mental health condition. Often it is difficult for individuals to physically speak about their mental health condition, and social media offers a less direct platform for identifying an illness they suffer from. By using previous tweets, a model was built that analyzes the language of tweets to identify users who have a mental health disorder, and can accurately diagnose them.[4] Through this model, it has shown how many people actually suffer from a disorder, and reveals those who have more than one disorder as certain conditions often present themselves together. For example, users identified with obsessive compulsive disorder (OCD) were more likely to also have schizophrenia as well, as compared to other users. Anxiety, depression, eating disorders, and bipolar disorder were also grouped together. This new research gives medical professionals access to a large amount of previously inaccessible data that can be used to effectively determine mental health disorders among the population.
Adverse Drug Reactions and Online Forums edit
When taking medication for a condition, experiencing an unforeseen and unwanted side effect is call an adverse drug reaction (ADR). ADR are a leading cause of death globally, and can also cause serious lifelong disabilities or impairments. In the United States, many drugs are approved before all known ADRs are discovered. Physicians and other medical professionals then report ADRs when they appear in their patients. There currently is no reliable system for sorting through consumer comments and descriptions of the symptoms that they are experiencing. Like in the case of mental health, individuals often turn to social media, posting about their condition, often trying to self-diagnose. These posts are invaluable. Most feedback about drugs is given by the physician or other medical professional, in the formal medical jargon. These posts, on sites like MedHelp.org, detail symptoms, pain, and how exactly the patient is feeling, without a potential misinterpretation by their doctor. Techniques have been developed that will allow researchers to utilize patient posts. This assists the medical community in assessing the benefits and the side-effects of certain drugs, and determine if that particular drug is too dangerous for human use.
References edit
- ^ Fuchs, Georg, et al. "Tracing the German centennial flood in the stream of tweets: first lessons learned." Proceedings of the second ACM SIGSPATIAL international workshop on crowdsourced and volunteered geographic information. ACM, 2013.
- ^ Tumasjan, Andranik, et al. "Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment." ICWSM 10 (2010): 178-185., additional text.
- ^ Bollen, Johan, Huina Mao, and Xiaojun Zeng. "Twitter mood predicts the stock market." Journal of Computational Science 2.1 (2011): 1-8.
- ^ a b Coppersmith, Glen, et al. "From ADHD to SAD: Analyzing the language of mental health on Twitter through self-reported diagnoses." Proceedings of the 2nd Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality. 2015.
- ^ Yang, Ming, Melody Kiang, and Wei Shang. "Filtering big data from social media–Building an early warning system for adverse drug reactions." Journal of biomedical informatics 54 (2015): 230-240.
- ^ a b Gleason, Benjamin. "# Occupy Wall Street: Exploring informal learning about a social movement on Twitter." American Behavioral Scientist (2013): 0002764213479372.