We Translate Data into Actionable Insights

The Data Innovation Group comprises top researchers and practitioners from the Lee Kuan Yew School of Public Policy and the Lloyd's Register Foundation Institute for the Public Understanding of Risk, National University of Singapore

Highlights

Public Interest in Research on Aging: Analysis of Altmetric Attention Scores Over 5 Years

Abstract: This study pinpoints the areas in gerontological research that have commanded the most public attention as scored by Altmetric. We collected 12,740 outputs published in gerontological journals over 5 years from 2017 to 2022. Next, we identified the top 5% of outputs with the highest Altmetric Attention Scores. After removing duplicate publications and those without an abstract, 588 outputs remained. Our content analysis of the abstracts was conducted deductively and inductively. Theme 1 (50%; N = 293) revolved around “Health and Well-Being” (Theme 1). Theme 2 (28%; N = 164) was about “Health Services, Interventions and Innovations.” Theme 3 was about “Informal and Long-Term Care” (13%; N = 80). Theme 4 focused on “Structural Inequalities” affecting older adults (9%; N = 51). Gerontologists should strengthen collaboration with other stakeholders to better engage the public across different age-related issues. These partnerships will enable the creation of initiatives that better serve the needs of aging populations.

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Social media discourse on ageism, sexism, and racism: Analysis of 150 million tweets over 15 years

Abstract: Ageism is a major but oft-overlooked social determinant of health. In fact, it is widely accepted among scholars that ageism is one of the least acknowledged forms of inequality, although few empirical attempts have been made to substantiate this claim. This is the first study that quantifies the amount of discourse dedicated to ageism, sexism, and racism on Twitter. Specifically, we rely on the usage of hashtags as a proxy for the frequency of discussions surrounding each form of inequality over a 15-year period from 2007 to 2022. We also identify key events that triggered spikes in Twitter activity for each form of inequality. Hashtags related to racism and sexism were extracted from past scholarship. We also employed a snowball sampling method whereby we queried the hashtags using Twitter's search function to identify other hashtags. As limited research has been conducted on ageism-related hashtags, we queried hashtags utilized by advocacy groups and adopted a snowball sampling method to compile other relevant hashtags. Tweets collected (N = 154,353,047) spanned 15 years, from August 23, 2007 to December 31, 2022. From 2007 to 2022, racism-related hashtags were used the most, followed by sexism-related hashtags and ageism-related hashtags. Racism-related hashtags (N = 99,250,348) were mentioned about 60 times more than ageism-related hashtags (N = 1,648,926). Sexism-related hashtags (N = 38,933,113) were mentioned 24 times more than ageism-related hashtags. The increasing linear trend of tweets associated with ageism (p < 0.001), sexism (p < 0.05), and racism (p < 0.05) reached significance. Incidents of racism and sexism often generated widespread public outrage. Conversely, instances of ageism rarely caused spikes in social media activity. Rather, these spikes were mainly observed during events such as the release of a report on ageism, a conference related to aging, or observances such as International Day of Older Persons. There is a need to hasten moves to raise awareness of ageism. To ensure that discussions on ageism are not confined to academic and policy circles, advocacy campaigns could be held to educate the public on the issue and its negative concomitants.

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Questions about Aging and Later Life on Quora

Abstract: Gerontologists have yet to explore the types of questions individuals have about later life. Analyzing questions offers a unique perspective on how individuals make sense of age-related issues. Specifically, questions require people to articulate specific inquiries or doubts, thus providing an unfiltered glimpse into the public’s concerns and priorities vis-à-vis aging. We conduct a content analysis of questions posted on Quora that pertain to later life. We compiled 2,950 questions posted across four topics on Quora: ‘Aging’, ‘Senior Citizens’, ‘Elders’ and ‘Older People’. After applying our exclusion criteria, 658 questions were left for content analysis. These questions received over 250 million views. Both deductive and inductive approaches guided our qualitative analysis. Five themes emerged from our analysis. The biggest theme (30%; N=195) dealt with ‘Practical Concerns’ (Theme 1). The next biggest theme (29%; N=191) was about ‘Health and Well-Being’ (Theme 2). Theme 3 was about the ‘Prolongation of Youth’ (16%; N=110) and Theme 4 was about the ‘Science of Aging’ (15%; N=97). Theme 5 covered ‘Existential Concerns’ (10%; N=65). There is a need to address concerns that the public has about aging, particularly those involving practical issues and health. Growing old is unavoidable and with the population aging at a rapid pace, assuaging such concerns is of paramount importance. By doing so, individuals can approach the aging process with greater clarity and an elevated sense of empowerment.

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Advocating for Older Adults in the Age of Social Media: Strategies to Achieve Peak Engagement on Twitter

Abstract: Over the last decade, many organizations dedicated to serving the needs and interests of older adults have turned to social media platforms, such as Twitter, subsequently rebranded X, to improve the visibility of age-related issues. However, notwithstanding their growing digital presence and participation, minimal attention has been paid to the use of social media among these advocacy groups. To achieve policy change, advocacy organizations must first be able to engage and mobilize audiences. Our study aims to elucidate how different tweet features affect the time it takes for posts uploaded by age advocacy organizations to reach peak engagement. We collated 204,905 tweets from 53 age advocacy organizations posted over a 12-year period. The engagement score of each tweet was calculated by combining well-established metrics, namely likes, retweets, quote tweets, and replies. We ran Cox models with tweet features as predictors and time-to-peak engagement as the outcome. “Peak engagement” (event) refers to engagement scores above the 75th percentile, and “time” refers to months taken to reach peak engagement per tweet. Approximately 1 in 2 tweets (n=103,068, 50.3%) had either no hashtags or just 1 hashtag. Around two-thirds (n=131,220, 64%) of the tweets included a URL. Visual information was highly underused, with most tweets not including GIFs (n=204,202, 99.7%), videos (n=199,800, 97.5%), or photos (n=143,844, 70.2%). Roughly half (n=101,470, 49.5%) of the tweets contained mentions and 9.3% (n=19,009) of tweets were replies. Only 4.5% (n=9285) of tweets were quote tweets. Most tweets were uploaded in the afternoon (n=86,004, 42%) and on a weekday (n=180,499, 88.1%). As hypothesized, features associated with peak engagement were the inclusion of visual elements like photos, which increased peak engagement by 3 times (P<.001), and the use of 3 or more hashtags (P<.001). Quote tweets increased engagement by 3 times (P<.001), as compared to regular tweets, controlling for account-level covariates. Tweets from organizations with a higher tweet volume were 40% less likely to reach peak engagement (P<.001). Social media as a networked platform has the potential to reach users on a global scale and at an exponential speed. Having uncovered the features that are more likely to reach peak engagement on Twitter, our study serves as an invaluable resource for age advocacy organizations in their movement to create a more age-inclusive world.

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News media coverage of LGBT identities over 10 years in a 400-million-word corpus

Abstract: This study is the first to analyze LGBT portrayals in a news media dataset over a decade (2010–2020). We selected Singapore as a country of interest, emblematic of a nation grappling with state-encouraged heteronormativity and a remnant colonial law against homosexuality (377A), fraught with calls for its repeal that was only enacted in 2022. Our study is interested in this period bookended by challenge and change, particularly in newspaper portrayals of LGBT narratives. Newspapers are an important source of current information and have the power to shape societal perceptions. We lay the groundwork and provide a framework to analyze news media narratives of other Commonwealth nations with colonial pasts and inherited laws criminalizing LGBT communities. This study analyzes LGBT portrayals in a 400-million-word news media dataset over a decade (2010–2020). First, we aimed to track the volume of LGBT media coverage over time and elucidate differences in coverage of different identity markers. Second, we aimed to track sentiments on LGBT portrayals. Third, we aimed to track salient narratives circulated about LGBT stories. The study leveraged a 400-million-word corpus from news media in Singapore, identifying the following target keywords: LGBT, Lesbian, Gay, Bisexual, Transgender, Pink Dot (a local Pride event), 377A. First, coverage volume was tracked using annual changes in keyword mentions per million, elucidating differences in coverage of different sub-groups. Second, sentiment analysis on a valence scale was conducted on LGBT collocates. Third, we distilled salient narratives about LGBT identities using thematic labelling of top-frequency collocates. First, overall coverage of LGBT steadily increased over the decade, though Gay identities evidenced asymmetrical coverage—outstripping ‘Bisexual’ keywords by seven times, ‘Lesbian’ by four, ‘Transgender’ by two. Second, sentiment scores for Pink Dot (a local pride event) were most positive; Lesbian, Gay, LGBT, Transgender were neutral; Bisexual and 377A dipped slightly negative. Third, topics differed across the four identities: uniquely, ‘Lesbian’ collocates related to sensationalized cinema; ‘Gay’ about hate crimes; ‘Bisexual’ about population surveys; ‘Transgender’ about challenges (transitioning, alienation, suicide). Practically, we presented a decade-long barometer of LGBT sentiments and themes on a national level, providing a framework to analyze media for more effective communication strategies—applicable to Commonwealth countries with similar inherited colonial laws. Salient repetition through media association may unwittingly frame certain issues negatively; caution is prudent in representing each sub-group adequately, rather than portraying the LGBT identity as monolithic.

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#ProtectOurElders: Analysis of Tweets about Older Asian Americans and Anti-Asian Sentiments During the COVID-19 Pandemic

Abstract: A silver lining to the COVID-19 pandemic is that it cast a spotlight on a long-underserved group. The barrage of attacks against older Asian Americans during the crisis galvanized society into assisting them in various ways. On Twitter, now known as X, support for them coalesced around the hashtag #ProtectOurElders. To date, discourse surrounding older Asian Americans has escaped the attention of gerontologists—a gap we seek to fill. Our study serves as a reflection of the level of support that has been extended to older Asian Americans, even as it provides timely insights that will ultimately advance equity for them. This study explores the kinds of discourse surrounding older Asian Americans during the COVID-19 crisis, specifically in relation to the surge in anti-Asian sentiments. The following questions guide this study: What types of discourse have emerged in relation to older adults in the Asian American community and the need to support them? How do age and race interact to shape these discourses? What are the implications of these discourses for older Asian Americans? We retrieved tweets (N=6099) through 2 search queries. For the first query, we collated tweets with the hashtag #ProtectOurElders. For the second query, we collected tweets with an age-based term, for example, “elderly” or “old(er) adults(s)” and either the hashtag #StopAAPIHate or #StopAsianHate. Tweets were posted from January 1, 2020, to August 1, 2023. After applying the exclusion criteria, the final data set contained 994 tweets. Inductive and deductive approaches informed our qualitative content analysis. A total of 4 themes emerged, with 50.1% (498/994) of posts framing older Asian Americans as “vulnerable and in need of protection” (theme 1). Tweets in this theme either singled them out as a group in need of protection because of their vulnerable status or discussed initiatives aimed at safeguarding their well-being. Posts in theme 2 (309/994, 31%) positioned them as “heroic and resilient.” Relevant tweets celebrated older Asian Americans for displaying tremendous strength in the face of attack or described them as individuals not to be trifled with. Tweets in theme 3 (102/994, 10.2%) depicted them as “immigrants who have made selfless contributions and sacrifices.” Posts in this section referenced the immense sacrifices made by older Asian Americans as they migrated to the United States, as well as the systemic barriers they had to overcome. Posts in theme 4 (85/994, 8.5%) venerated older Asian Americans as “worthy of honor.” The COVID-19 crisis had the unintended effect of garnering greater support for older Asian Americans. It is consequential that support be extended to this group not so much by virtue of their perceived vulnerability but more so in view of their boundless contributions and sacrifices.

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Powerful tool or too powerful? Early public discourse about ChatGPT across 4 million tweets

Abstract: This paper investigates initial exuberance and emotions surrounding ChatGPT’s first three months of launch (1 December 2022–1 March 2023). The impetus for studying active discussions surrounding its implications, fears, and opinions is motivated by its nascent popularity and potential to disrupt existing professions; compounded by its significance as a crucial inflexion point in history. Capturing the public zeitgeist on new innovations—much like the advent of the printing press, radio, newspapers, or the internet—provides a retrospective overview of public sentiments, common themes, and issues. Since launch, few big data studies delved into initial public discourse surrounding the chatbot. This report firstly identifies highest-engagement issues and themes that generated the most interaction; secondly, identifies the highest-engaged keywords on both sides of the sentiment valence scale (positive and negative) associated with ChatGPT. We interrogate a large twitter corpus (n = 4,251,662) of all publicly available English-language tweets containing the ChatGPT keyword. Our first research aim utilizes a prominent peaks model (upper-quartile significance threshold of prominence>20,000). Our second research aim utilized sentiment analysis to identify, week-on-week, highest-frequency negative, and positive keywords and emojis. Six prominent peaks were identified with the following themes: ‘hype and hesitance’, ‘utility and misuse in professional and academic settings’, ‘demographic bias’, ‘philosophical thought experiments on morality’ and ‘artificial intelligence as a mirror of human knowledge’. Of high-frequency valence, negativity included credibility concerns, implicit bias, environmental ethics, employment rights of data annotators and programmers, the ethicality of neural network datasets. Positivity included excitement over application, especially in coding, as a creative tool, education, and personal productivity. Overall, sentiments and themes were double-edged, expressing excitement over this powerful new tool and wariness toward its potential for misuse.
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