Twitter-derived measures of sentiment towards minorities (2015–2016) and associations with low birth weight and preterm birth in the United States

Nguyen, Thu T.; Meng, Hsien Weng; Sandeep, Sanjeev; McCullough, M.; Yu, Weijun; Lau, Yan Kwan; Huang, Dina; Nguyen, Quynh C.

doi:10.1016/j.chb.2018.08.010

Cited by 36 publications

(59 citation statements)

References 43 publications

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“…These physiological relationships tied to social isolation feel particularly consequential in light of the racial differences identified in this study in the degree to which stigma was felt, and its impact on social support—with Black and Hispanic women in particular more likely to describe stronger stigma tied to reduction in support received. Given what we know about racial disparities in pregnancy and birth outcomes[e.g., [30, 31]], and their ties to chronic and acute stress [32, 33], it is possible that some aspect of this pathway may be mediated by or partially determined by experiences of stigma, and in turn, ties to social support [34]. Further research could explore how this stigma may relate to or be caused by racism, and particularly on how, on a societal level, fertility is valued differentially by race/ethnicity.…”

Section: Discussionmentioning

confidence: 99%

“…Society is, at the end of the day, still going to stigmatize you no matter which way”: A qualitative study of the impact of stigma on social support during unintended pregnancy in early adulthood

et al. 2019

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Unintended pregnancy in adolescence and early adulthood is stigmatized in the United States because it deviates from social norms that consider young people’s sexuality as a social problem. While limited, prior research has found that this stigma prevents young people from telling people in their lives about their pregnancies, for fear of judgment or negative reactions. We hypothesized that this selective disclosure of unintended pregnancy due to stigma would reduce the social support available to young pregnant people at a particularly vulnerable time—social support that we know is important for optimal physical and mental health of the young person, and the pregnancy (should they choose to carry to term). To explore this hypothesis, we conducted a qualitative study among young people to understand if and how they experienced stigma in relation to an unintended pregnancy, how this stigma shaped patterns of pregnancy disclosure, the implications for received social support, and participant thoughts on how to alleviate the influence of this stigma on their lives. In in-depth interviews with 25 young people in the San Francisco Bay area who had experienced at least one unintended pregnancy, using a thematic analysis approach, we found that the stigma of unintended pregnancy led participants to selectively disclose the pregnancy to limited people, which in turn cut them off from needed sources of social support. Black and Hispanic women disproportionately described this experience. Participants expressed a desire for programs that would connect young people who had experienced unplanned pregnancy to each other–either via the internet, organized groups through clinical care sites, college or high school campuses, or other forums—as a way to alleviate stigma, share perspectives and lessons learned, and otherwise build emotional and informational support networks for themselves where their usual support had fallen away.

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Section: Discussionmentioning

confidence: 99%

“…Society is, at the end of the day, still going to stigmatize you no matter which way”: A qualitative study of the impact of stigma on social support during unintended pregnancy in early adulthood

et al. 2019

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“…Below, we describe results from our quantitative analyses, based on the SGD algorithmic classification of the sentiment (positive vs. neutral/negative) of 1,249,653 tweets containing at least one of the relevant keywords pertaining to a racial or ethnic minority group. Descriptive details of these tweets have been previously described [37]. Briefly, approximately 620,000 tweets were about Blacks, 205,000 about Hispanics, 270,000 about Asians, and 60,000 about Middle Eastern groups.…”

Section: Resultsmentioning

confidence: 99%

“…For example, Eichstaedt et al found greater use of anger, negative emotion, and disengagement words on Twitter predicted county-level heart disease mortality [38]. Notably, previous research found more negative area-level sentiment towards blacks and Middle Eastern groups was related to worse individual-level birth outcomes, and this was true for the full population and for racial minorities [37]. Thus, a hostile social climate related to race may have implications for health and well-being, but developing valid and inexpensive measures remains an important research challenge.…”

Section: Discussionmentioning

confidence: 99%

Pride, Love, and Twitter Rants: Combining Machine Learning and Qualitative Techniques to Understand What Our Tweets Reveal about Race in the US

Nguyen

Criss

Allen

et al. 2019

IJERPH

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Objective: Describe variation in sentiment of tweets using race-related terms and identify themes characterizing the social climate related to race. Methods: We applied a Stochastic Gradient Descent Classifier to conduct sentiment analysis of 1,249,653 US tweets using race-related terms from 2015–2016. To evaluate accuracy, manual labels were compared against computer labels for a random subset of 6600 tweets. We conducted qualitative content analysis on a random sample of 2100 tweets. Results: Agreement between computer labels and manual labels was 74%. Tweets referencing Middle Eastern groups (12.5%) or Blacks (13.8%) had the lowest positive sentiment compared to tweets referencing Asians (17.7%) and Hispanics (17.5%). Qualitative content analysis revealed most tweets were represented by the categories: negative sentiment (45%), positive sentiment such as pride in culture (25%), and navigating relationships (15%). While all tweets use one or more race-related terms, negative sentiment tweets which were not derogatory or whose central topic was not about race were common. Conclusion: This study harnesses relatively untapped social media data to develop a novel area-level measure of social context (sentiment scores) and highlights some of the challenges in doing this work. New approaches to measuring the social environment may enhance research on social context and health.

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“…The data is free, publicly available, and considered as an important source of information for researchers from different disciplines. Many previous studies collected information from ACS and leveraged with Twitter data to analyze sentiment of the people in the arena of public health [28], urban spaces [38], politics [39,40], disasters management [41], racial conflicts [30,42], and gender disparity [43]. Thus, linking Twitter data with Coronavirus data is a common practice among the researchers to evaluate the impacts of socioeconomic and demographic characteristics on the sentiments of the people towards a subject of interest.…”

Section: Us Census Data and Socioeconomic Analysismentioning

confidence: 99%

Twitter and Census Data Analytics to Explore Socioeconomic Factors for Post-COVID-19 Reopening Sentiment

Rahman

Ali

et al. 2020

SSRN Journal

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Investigating and classifying sentiments of social media users (e.g., positive, negative) towards an item, situation, and system are very popular among the researchers. However, they rarely discuss the underlying socioeconomic factor associations for such sentiments. This study attempts to explore the factors associated with positive and negative sentiments of the people about reopening the economy, in the United States (US) amidst the COVID-19 global crisis. It takes into consideration the situational uncertainties (i.e., changes in work and travel pattern due to lockdown policies), economic downturn and associated trauma, and emotional factors such as depression. To understand the sentiment of the people about the reopening economy, Twitter data was collected, representing the 51 states including Washington DC of the US. Statewide socioeconomic characteristics of the people (e.g., education, income, family size, and employment status), built environment data (e.g., population density), and the number of COVID-19 related cases were collected and integrated with Twitter data to perform the analysis. A binary logit model was used to identify the factors that influence people toward a positive or negative sentiment. The results from the logit model demonstrate that family households, people with low education levels, people in the labor force, low-income people, and people with higher house rent are more interested in reopening the economy. In contrast, households with a high number of members and high income are less interested to reopen the economy. The accuracy of the model is good (i.e., the model can correctly classify 56.18% of the sentiments). The Pearson chi2 test indicates that overall this model has high goodness-of-fit. This study provides a clear indication to the policymakers where to allocate resources and what policy options they can undertake to improve the socioeconomic situations of the people and mitigate the impacts of pandemics in the current situation and as well as in the future.

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Twitter-derived measures of sentiment towards minorities (2015–2016) and associations with low birth weight and preterm birth in the United States

Cited by 36 publications

References 43 publications

“…Society is, at the end of the day, still going to stigmatize you no matter which way”: A qualitative study of the impact of stigma on social support during unintended pregnancy in early adulthood

“…Society is, at the end of the day, still going to stigmatize you no matter which way”: A qualitative study of the impact of stigma on social support during unintended pregnancy in early adulthood

Pride, Love, and Twitter Rants: Combining Machine Learning and Qualitative Techniques to Understand What Our Tweets Reveal about Race in the US

Twitter and Census Data Analytics to Explore Socioeconomic Factors for Post-COVID-19 Reopening Sentiment

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