Salvatore Giorgi

Salvatore Giorgi
University of Pennsylvania | UP · Department of Psychology

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48
Publications
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558
Citations

Publications

Publications (48)
Article
Background: Research conducted during the COVID-19 Pandemic has identified two co-occurring public health concerns: loneliness and substance use. Findings from research conducted prior to the pandemic are inconclusive as to the links between loneliness and substance use. This study aimed to measure associations of loneliness with three different t...
Article
Full-text available
How we perceive our surrounding world impacts how we live in and react to it. In this study, we propose LaBel (Latent Beliefs Model), an alternative to topic modeling that uncovers latent semantic dimensions from transformer-based embeddings and enables their representation as generated phrases rather than word lists. We use LaBel to explore the ma...
Article
Background: Assessing risk for excessive alcohol use is important for applications ranging from recruitment into research studies to targeted public health messaging. Social media language provides an ecologically embedded source of information for assessing individuals who may be at risk for harmful drinking. Methods: Using data collected on 36...
Preprint
Background Digital technology, the internet and social media are increasingly investigated as a promising means for monitoring symptoms and delivering mental health treatment. These apps and interventions have demonstrated preliminary acceptability and feasibility, but previous reports suggests that access to technology may still be limited among i...
Article
Full-text available
Background & Aims Previous studies have shown that nonsuicidal self-injury (NSSI) has addictive features, and an addiction model of NSSI has been considered. Addictive features have been associated with severity of NSSI and adverse psychological experiences. Yet, there is debate over the extent to which NSSI and substance use disorders (SUDs) are s...
Preprint
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How does language differ across one's Facebook status updates vs. one's text messages (SMS)? In this study, we show how Facebook and SMS use differs in psycho-linguistic characteristics and how these differences drive downstream analyses with an illustration of depression diagnosis. We use a sample of consenting participants who shared Facebook sta...
Preprint
Full-text available
The word embedding association test (WEAT) is an important method for measuring linguistic biases against social groups such as ethnic minorities in large text corpora. It does so by comparing the semantic relatedness of words prototypical of the groups (e.g., names unique to those groups) and attribute words (e.g., 'pleasant' and 'unpleasant' word...
Article
Full-text available
Significance On May 25, 2020, George Floyd, an unarmed Black American male, was murdered by a White police officer in Minneapolis. Footage of his death was widely shared and caused widespread protests. Using data from Gallup before and after his death, we found an unprecedented level of anger and sadness in the population, particularly among Black...
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Objective We explore the personality of counties as assessed through linguistic patterns on social media. Such studies were previously limited by the cost and feasibility of large-scale surveys; however, language-based computational models applied to large social media datasets now allow for large-scale personality assessment. Method We applied a...
Article
Full-text available
Technology now makes it possible to understand efficiently and at large scale how people use language to reveal their everyday thoughts, behaviors, and emotions. Written text has been analyzed through both theory-based, closed-vocabulary methods from the social sciences as well as data-driven, open-vocabulary methods from computer science, but thes...
Preprint
BACKGROUND Digital technology, the internet and social media are increasingly investigated as a promising means for monitoring symptoms and delivering mental health treatment. These apps and interventions have demonstrated preliminary acceptability and feasibility, but previous reports suggests that access to technology may still be limited among i...
Article
Background Digital technology, the internet, and social media are increasingly investigated as promising means for monitoring symptoms and delivering mental health treatment. These apps and interventions have demonstrated preliminary acceptability and feasibility, but previous reports suggest that access to technology may still be limited among ind...
Preprint
Full-text available
The language that individuals use for expressing themselves contains rich psychological information. Recent significant advances in Natural Language Processing (NLP) and Deep Learning (DL), namely transformers, have resulted in large performance gains in tasks related to understanding natural language such as machine translation. However, these sta...
Preprint
UNSTRUCTURED As of December 2020, the SARS-CoV-2 virus has been responsible for over 78 million cases of COVID-19 worldwide, resulting in over 1.7 million deaths. In the United States in particular, protective measures against the COVID-19 pandemic have been hampered by political polarization and discrepancies among federal, state, and local polici...
Article
Unstructured: By March 2021, the SARS-CoV-2 virus has been responsible for over 115 million cases of COVID-19 worldwide, resulting in over 2.5 million deaths. As the virus grew exponentially, so did its media coverage, resulting in a proliferation of conflicting information on social media platforms - a so-called "infodemic." In this mixed scoping...
Article
Users’ information-seeking and information-sharing behavior provide socioeconomic and psychological insights that are useful to understand regional trends in health. We study the spatial variations in aggregate Google Search and Twitter trends across 208 Designated Market Areas (DMAs) in the United States and their association with regional health....
Preprint
Full-text available
Technology now makes it possible to understand efficiently and at large scale how people use language to reveal their everyday thoughts, behaviors, and emotions. Written text has been analyzed through both theory-based, closed-vocabulary methods from the social sciences as well as data-driven, open-vocabulary methods from computer science, but thes...
Conference Paper
Full-text available
The novelty and global scale of the COVID-19 pandemic has lead to rapid societal changes in a short span of time. As government policy and health measures shift, public perceptions and concerns also change, an evolution documented within discourse on social media. We propose a dynamic content-specific LDA topic modeling technique that can help to i...
Preprint
Full-text available
Black Lives Matter (BLM) is a grassroots movement protesting violence towards Black individuals and communities with a focus on police brutality. The movement has gained significant media and political attention following the killings of Ahmaud Arbery, Breonna Taylor, and George Floyd and the shooting of Jacob Blake in 2020. Due to its decentralize...
Article
Full-text available
Researchers and policy makers worldwide are interested in measuring the subjective well-being of populations. When users post on social media, they leave behind digital traces that reflect their thoughts and feelings. Aggregation of such digital traces may make it possible to monitor well-being at large scale. However, social media-based methods ne...
Preprint
While most mortality rates have decreased in the US, maternal mortality has increased and is among the highest of any OECD nation. Extensive public health research is ongoing to better understand the characteristics of communities with relatively high or low rates. In this work, we explore the role that social media language can play in providing i...
Article
Background Substance use by youth remains a significant public health concern. Social media provides the opportunity to discuss and display substance use–related beliefs and behaviors, suggesting that the act of posting drug-related content, or viewing posted content, may influence substance use in youth. This aligns with empirically supported theo...
Preprint
Social media is increasingly used for large-scale population predictions, such as estimating community health statistics. However, social media users are not typically a representative sample of the intended population --- a "selection bias". Across five tasks for predicting US county population health statistics from Twitter, we explore standard r...
Article
Background: Recovery support services, including in vivo (i.e., face to face) peer-based supports and social networks, are associated with positive effects on substance use disorder recovery outcomes. The translation of in vivo supports to digital platforms is a recent development that is mostly unexamined. The types of users and their engagement...
Article
Full-text available
Beck’s insight—that beliefs about one’s self, future, and environment shape behavior—transformed depression treatment. Yet environment beliefs remain relatively understudied. We introduce a set of environment beliefs— primal world beliefs or primals —that concern the world’s overall character (e.g., the world is interesting, the world is dangerous...
Preprint
Full-text available
Nowcasting based on social media text promises to provide unobtrusive and near real-time predictions of community-level outcomes. These outcomes are typically regarding people, but the data is often aggregated without regard to users in the Twitter populations of each community. This paper describes a simple yet effective method for building commun...
Preprint
Full-text available
Predictive models over social media language have shown promise in capturing community outcomes, but approaches thus far largely neglect the socio-demographic context (e.g. age, education rates, race) of the community from which the language originates. For example, it may be inaccurate to assume people in Mobile, Alabama, where the population is r...
Conference Paper
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This article is a system description and report on the submission of a team from the University of Pennsylvania in the 'CLPsych 2018' shared task. The goal of the shared task was to use childhood language as a marker for both current and future psychological health over individual lifetimes. Our system employs multiple textual features derived from...
Article
Full-text available
Objectives The current study analyzes a large set of Twitter data from 1,384 US counties to determine whether excessive alcohol consumption rates can be predicted by the words being posted from each county. Methods Data from over 138 million county-level tweets were analyzed using predictive modeling, differential language analysis, and mediating...
Preprint
Full-text available
A recent preprint by Brown and Coyne titled, "No Evidence That Twitter Language Reliably Predicts Heart Disease: A Reanalysis of Eichstaedt et al." asserts to re-analyze our 2015 article published in Psychological Science, “Twitter Language Predicts Heart Disease Mortality”, disputing its primary findings. While we welcome scrutiny of the study, Br...
Article
People associate certain behaviors with certain social groups. These stereotypical beliefs consist of both accurate and inaccurate associations. Using large-scale, data-driven methods with social media as a context, we isolate stereotypes by using verbal expression. Across four social categories—gender, age, education level, and political orientati...
Conference Paper
Full-text available
We investigate whether psychological well-being translates across English and Span-ish Twitter, by building and comparing source language and automatically translated weighted lexica in English and Spanish. We find that the source language models perform substantially better than the machine translated versions. Moreover, manually correcting transl...
Conference Paper
Research into the darker traits of human nature is growing in interest especially in the context of increased social media usage. This allows users to express themselves to a wider online audience. We study the extent to which the standard model of dark personality -- the dark triad -- consisting of narcissism, psychopathy and Machiavellianism, is...
Conference Paper
Full-text available
In this paper, an Adaptive Neural Control (ANC) architecture is used for system replication and control within a Resilient Control framework. A dynamic model is chosen for our plant and a “maliciously attacked” plant. A Model Reference Adaptive Control (MRAC) architecture is used to replicate and control the plant to match an ideal reference system...

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Project
Research has defined stress as a state where an individual interacts and attempts to cope with a negative stimulus while experiencing distress in the process (Cohen, Miller, & Rabin, 2001). Stress has been found to negatively affect job performance, to be a key predictor of burnout, and impair acute (e.g. blood pressure spikes, immune deficiency) as well as chronic (e.g. cardiovascular disease, hypertension, & depression) health and well-being, as well as psychological functioning and mental processing (Staal, 2004). People commonly complain about being stressed out by their jobs, families, but also by political or social events, and even self-imposed deadlines not only in their private conversations, but also on social media. We explored the behavioral manifestations of stress by examining the characteristic linguistic patterns associated with perceived high versus low stress. 2,800 participants completed an established stress scale (Cohen, 2007) and shared their Facebook data. We show the linguistic patterns characteristic of individuals high and low in stress, as well as differential patterns of high and low stress in different age groups and by gender. Individuals higher in stress discuss somatic concerns (pain, illness, suffering), negative affect (sadness, anger), and disclose lack of motivation, lack of control over life circumstances and relationships, as well as regret, and appear to be highly self-critical. Those lower in perceived stress discuss topics around self-care (e.g. getting sleep, spending time outdoors), social events and outings with friends, language suggesting control over life circumstances, success and productivity, vacation and relaxation, positive affect and enjoyment of life. We further develop a social-media language-based predictive model for stress and then apply this Facebook-based model to geotagged Tweets to identify more vs. less stressed counties. Last, we examine correlates of stress on the macro level and gain insights into communities that are marked by a culture of stress.