Seasonality Pattern of Suicides in the US – a Comparative Analysis of a Twitter Based Bad-mood Index and Committed Suicides

Kmetty, Zoltán; Koltai, Júlia; Bokányi, Eszter; Bozsonyi, Károly

doi:10.17356/ieejsp.v3i1.302

Cited by 7 publications

(9 citation statements)

References 19 publications

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“…Thus, we could not capture the same temporal dynamics in social media as we can observe in the case of committed suicides. This confirms previous results (Kmetty et al 2017), where the authors drew a similar conclusion analyzing Twitter data.…”

Section: Discussionsupporting

confidence: 91%

“…They used the data of only 17 at-risk participants for fitting their suicide-risk classification model. Kmetty et al (2017) studied the temporal relationships between the moods of tweets and the number of committed suicides. They filtered more than 626 million tweets between February 1, 2012, and August 31, 2016, geolocated for the USA to identify those containing one of the following words, "depression," "depressed," "suicide," "Prozac," or "Zoloft."…”

Section: Recent Studies Of the Fieldmentioning

confidence: 99%

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From Durkheim to Machine Learning: Finding the Relevant Sociological Content in Depression and Suicide-Related Social Media Discourses

Koltai

Kmetty

Bozsonyi

2021

Pathways Between Social Science and Computational Social Science

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The phenomenon of suicide has been a focal point since Durkheim among social scientists. Internet and social media sites provide new ways for people to express their positive feelings, but they are also platforms to express suicide ideation or depressed thoughts. Most of these posts are not about real suicide, and some of them are a cry for help. Nevertheless, suicide- and depression-related content varies among platforms, and it is not evident how a researcher can find these materials in mass data of social media. Our paper uses the corpus of more than four million Instagram posts, related to mental health problems. After defining the initial corpus, we present two different strategies to find the relevant sociological content in the noisy environment of social media. The first approach starts with a topic modeling (Latent Dirichlet Allocation), the output of which serves as the basis of a supervised classification method based on advanced machine-learning techniques. The other strategy is built on an artificial neural network-based word embedding language model. Based on our results, the combination of topic modeling and neural network word embedding methods seems to be a promising way to find the research related content in a large digital corpus.Our research can provide added value in the detection of possible self-harm events. With the utilization of complex techniques (such as topic modeling and word embedding methods), it is possible to identify the most problematic posts and most vulnerable users.

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Section: Discussionsupporting

confidence: 91%

Section: Recent Studies Of the Fieldmentioning

confidence: 99%

From Durkheim to Machine Learning: Finding the Relevant Sociological Content in Depression and Suicide-Related Social Media Discourses

Koltai

Kmetty

Bozsonyi

2021

Pathways Between Social Science and Computational Social Science

Self Cite

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“…Here, we present the components of the time series analysis in the order shown: observations (mean polarity, similar to 3, trend in the time series, seasonality, and noise). We observe weekly seasonality, which is aligned with observed usage and mood patterns of Twitter by [ 12 ].…”

Section: Figuresupporting

confidence: 85%

“…The partial autocorrelation function, shown in Figure 4 , indicates a strong correlation with the time series and makes an early estimation for the possible coefficients for fitting a statistical model. While the objective of this study is not to perform forecasting, but to describe the series, this early test indicates that there are valuable information to gain and an early indication of seasonality, for the correlation repeats itself weekly, as was also observed by [ 12 ]. This is confirmed by the Box–Pierce test, which yields a very small p -value (of 2.2 e −16 ), suggesting a strong correlation.…”

Section: Resultsmentioning

confidence: 67%

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Monitoring the Emotional Response to the COVID-19 Pandemic Using Sentiment Analysis: A Case Study in Mexico

León-Sandoval

Zareei

Barbosa-Santillán

et al. 2022

Computational Intelligence and Neuroscience

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The world is facing the COVID-19 pandemic, leading to an unprecedented change in the lifestyle routines of millions. Beyond the general physical health, financial, and social repercussions of the pandemic, the adopted mitigation measures also present significant challenges in the population’s mental health and health programs. It is complex for public organizations to measure the population’s mental health in order to incorporate it into their own decision-making process. Traditional survey methods are time-consuming, expensive, and fail to provide the continuous information needed to respond to the rapidly evolving effects of governmental policies on the population’s mental health. A significant portion of the population has turned to social media to express the details of their daily life, rendering this public data a rich field for understanding emotional and mental well-being. This study aims to track and measure the sentiment changes of the Mexican population in response to the COVID-19 pandemic. To this end, we analyzed 760,064,879 public domain tweets collected from a public access repository to examine the collective shifts in the general mood about the pandemic evolution, news cycles, and governmental policies using open sentiment analysis tools. Sentiment analysis polarity scores, which oscillate around -0.15, show a weekly seasonality according to Twitter’s usage and a consistently negative outlook from the population. It also remarks on the increased controversy after the governmental decision to terminate the lockdown and the celebrated holidays, which encouraged the people to incur social gatherings. These findings expose the adverse emotional effects of the ongoing pandemic while showing an increase in social media usage rates of 2.38 times, which users employ as a coping mechanism to mitigate the feelings of isolation related to long-term social distancing. The findings have important implications in the mental health infrastructure for ongoing mitigation efforts and feedback on the perception of policies and other measures. The overall trend of the sentiment polarity is 0.0001110643.

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Using Big Data and Serverless Architecture to Follow the Emotional Response to the COVID-19 Pandemic in Mexico

León-Sandoval

Zareei

Barbosa-Santillán

et al. 2022

Communications in Computer and Information Science

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The emergence of the COVID-19 pandemic has led to an unprecedented change in the lifestyle routines of millions of people. Beyond the multiple repercussions of the pandemic, we are also facing significant challenges in the population’s mental health and health programs. Typical techniques to measure the population’s mental health are semiautomatic. Social media allow us to know habits and daily life, making this data a rich silo for understanding emotional and mental well-being. This study aims to build a resilient and flexible system that allows us to track and measure the sentiment changes of a given population, in our case, the Mexican people, in response to the COVID-19 pandemic. We built an extensive data system utilizing modern cloud-based serverless architectures to analyze 760,064,879 public domain tweets collected from a public access repository to examine the collective shifts in the general mood about the pandemic evolution, news cycles, and governmental policies using open sentiment analysis tools. We provide metrics, advantages, and challenges of developing serverless cloud-based architectures for a natural language processing project of a large magnitude.

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Seasonality Pattern of Suicides in the US – a Comparative Analysis of a Twitter Based Bad-mood Index and Committed Suicides

Cited by 7 publications

References 19 publications

From Durkheim to Machine Learning: Finding the Relevant Sociological Content in Depression and Suicide-Related Social Media Discourses

From Durkheim to Machine Learning: Finding the Relevant Sociological Content in Depression and Suicide-Related Social Media Discourses

Monitoring the Emotional Response to the COVID-19 Pandemic Using Sentiment Analysis: A Case Study in Mexico

Using Big Data and Serverless Architecture to Follow the Emotional Response to the COVID-19 Pandemic in Mexico

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