The unsuccessful ways to predict suicide by technology

Document Type:Coursework

Subject Area:Technology

Document 1

In the United States, it is estimated that the economic costs that come with deaths resulting from suicides are 26 billion US Dollars annually due to medical costs and absence from their respective jobs. Suicidal behavior is a sophisticated aspect consisting of a myriad of issues ranging from biological, social, psychological, social and clinical factors. Research has indicated that risk factors that accrue and lead to suicidal behavior can be detected and prevented. While the effects of suicides can be felt across the levels of society, it is crucial to investigate and come up with strategies that can be utilized to prevent suicide occurrences. Some researchers in their studies have allayed fears on the extent to which social media influences suicidal behaviors.

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The review began with reliance on keywords such as ‘Internet, social media, suicides, bullying, social networking, technology, suicide attempt, self-harm, prevention, and new technologies’. As the relevant literature appeared, we were able to get new information such as the place of publications of the articles. In addition to reliance on database keyword-searches, we used backward and forward referencing citation searches from the Google Scholar and Citethisforme websites for the articles identified earlier in this review. At the beginning of the review, a combination of high-level keywords was searched in the information sources that had been identified. The set of keywords were divided into two: technology-oriented words and domain oriented words. Additionally, online and social media-based interventions and technologies have the potential to reach a lot of people, particularly young people who may not be able to seek help from conventional sources.

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The major advantage that the new methodologies and technologies will have in the prevention of suicides is that these technologies will target a population that will otherwise be unreachable and to comes up with appropriate suicide prevention strategies O'Keeffe & Clarke-Pearson, 2011. Immediate interventions such as the one being used by Facebook may occur via the user information such as social posts, live streams, and other user-generated information. Fahey & Ueda (2018) studied the emotional responses in the social media platform; Twitter and utilized machine learning as a tool to track the Werther effect on social media. One of the ways to overcome the limitations experienced in the prevention strategies includes the rarity of the occurrences of events or a short time for observational periods (Fahey & Ueda, 2018).

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The study showed how the media by reporting cases of youth suicides broadens the ideation that suicide can be seen as an option and hence contributes independently to suicide problem (Mueller, 2017). Generally, the study contextualizes the role that social media has on the suicide problem and how suicide occurs in online social contexts. Mueller (2017) validates the use of technology and online tools to predict suicide ideation since conventional methods are not applicable in social media platforms. Two possible ways that the social media can enhance the risk of suicides is through cyberbullying (O'Keeffe & Clarke-Pearson, 2011) and providing access to information that can aid in committing suicide (Fernandes et al, 2018). One of the technologies that have been adopted in suicide prevention is the Samaritan’s radar as studied by (Coppersmith et al, 2018).

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The concept of text analysis is also crucial in predicting suicide behaviors. Text analysis uses software to categorize words used by individuals and evaluates the cognitive, psychological and social constructs that are exhibited from an individual’s verbal and written communication. This technology has the capabilities of searching an individual’s email as well as website postings to recognize individuals who are at risk of committing suicide (Luxton, June & Kinn, 2011). Such technologies are appropriate for websites where there are is a large set of data and can be used as large-scale automated surveillance for the social sites to identify users that are at potentially high risk (Luxton, June & Kinn, 2011). However, there are significant challenges in applying technologies in ways that will ensure confidentiality.

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The use of innovative technology that uses Artificial Intelligence and other program languages can help to detect the behavior of these individuals and can alert friends or authorities so that they can be helped. Technology-based programs also have limitations. Technology-based programs must encompass the aspect of communication habits exhibited by the users, however, not all users with suicide ideations will post information regarding their troubled life making it hard to predict suicide among them (Cheng, Chen & Yip, 2017). Also, the applications and software used for predicting suicides need to be culturally relevant and accepted by the end users. A good example is the Samaritan's application that was decommissioned due to strong public resistance (Coppersmith et al, 2018). , Leary, R. , Crutchley, P. , & Fine, A.

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