Ludovic Rheault

Background: The measurement and monitoring of generalized anxiety disorder requires frequent interaction with psychiatrists or psychologists. Access to mental health professionals is often difficult because of high costs or insufficient availability. The ability to assess generalized anxiety disorder passively and at frequent intervals could be a useful complement to conventional treatment and help with relapse monitoring. Prior work suggests that higher anxiety levels are associated with features of human speech. As such, monitoring speech using personal smartphones or other wearable devices may be a means to achieve passive anxiety monitoring. Objective: This study aims to validate the association of previously suggested acoustic and linguistic features of speech with anxiety severity. Methods: A large number of participants (n=2000) were recruited and participated in a single web-based study session. Participants completed the Generalized Anxiety Disorder 7-item scale assessment and provided an impromptu speech sample in response to a modified version of the Trier Social Stress Test. Acoustic and linguistic speech features were a priori selected based on the existing speech and anxiety literature, along with related features. Associations between speech features and anxiety levels were assessed using age and personal income as covariates. Results: Word count and speaking duration were negatively correlated with anxiety scores (r=–0.12; P<.001), indicating that participants with higher anxiety scores spoke less. Several acoustic features were also significantly (P<.05) associated with anxiety, including the mel-frequency cepstral coefficients, linear prediction cepstral coefficients, shimmer, fundamental frequency, and first formant. In contrast to previous literature, second and third formant, jitter, and zero crossing rate for the z score of the power spectral density acoustic features were not significantly associated with anxiety. Linguistic features, including negative-emotion words, were also associated with anxiety (r=0.10; P<.001). In addition, some linguistic relationships were sex dependent. For example, the count of words related to power was positively associated with anxiety in women (r=0.07; P=.03), whereas it was negatively associated with anxiety in men (r=–0.09; P=.01). Conclusions: Both acoustic and linguistic speech measures are associated with anxiety scores. The amount of speech, acoustic quality of speech, and gender-specific linguistic characteristics of speech may be useful as part of a system to screen for anxiety, detect relapse, or monitor treatment.

BACKGROUND The measurement and monitoring of generalized anxiety disorder requires frequent interaction with psychiatrists or psychologists. Access to mental health professionals is often difficult because of high costs or insufficient availability. The ability to assess generalized anxiety disorder passively and at frequent intervals could be a useful complement to conventional treatment and help with relapse monitoring. Prior work suggests that higher anxiety levels are associated with features of human speech. As such, monitoring speech using personal smartphones or other wearable devices may be a means to achieve passive anxiety monitoring. OBJECTIVE This study aims to validate the association of previously suggested acoustic and linguistic features of speech with anxiety severity. METHODS A large number of participants (n=2000) were recruited and participated in a single web-based study session. Participants completed the Generalized Anxiety Disorder 7-item scale assessment and provided an impromptu speech sample in response to a modified version of the Trier Social Stress Test. Acoustic and linguistic speech features were a priori selected based on the existing speech and anxiety literature, along with related features. Associations between speech features and anxiety levels were assessed using age and personal income as covariates. RESULTS Word count and speaking duration were negatively correlated with anxiety scores (r=–0.12; P<.001), indicating that participants with higher anxiety scores spoke less. Several acoustic features were also significantly (P<.05) associated with anxiety, including the mel-frequency cepstral coefficients, linear prediction cepstral coefficients, shimmer, fundamental frequency, and first formant. In contrast to previous literature, second and third formant, jitter, and zero crossing rate for the z score of the power spectral density acoustic features were not significantly associated with anxiety. Linguistic features, including negative-emotion words, were also associated with anxiety (r=0.10; P<.001). In addition, some linguistic relationships were sex dependent. For example, the count of words related to power was positively associated with anxiety in women (r=0.07; P=.03), whereas it was negatively associated with anxiety in men (r=–0.09; P=.01). CONCLUSIONS Both acoustic and linguistic speech measures are associated with anxiety scores. The amount of speech, acoustic quality of speech, and gender-specific linguistic characteristics of speech may be useful as part of a system to screen for anxiety, detect relapse, or monitor treatment.

Threats of social media manipulation during elections have become a central concern for modern democracies. This study tackles the problem of identifying the purpose and origins of social bots during electoral campaigns. We propose a methodology – uniform manifold approximation and projection combined with user-level document embeddings – that efficiently reveals the community structure of social media users. We show that this method can be used to predict the partisan affiliation of social media users with high accuracy, detect anomalous concentrations of social bots, and infer their geographical origin. We illustrate the methodology using Twitter data from the 2019 Canadian electoral campaign. Our evidence supports the thesis that social bots have become an integral component of campaign strategy for national actors. We also demonstrate how the methodology can be deployed to identify clusters of foreign bots, and we show that such accounts were used to share far-right and environment-related content during the campaign.

Contact tracing applications have been deployed at a fast pace around the world to stop the spread of COVID-19 and may be key to containing future pandemics. This study aims to explain public opinion toward cell phone contact tracing using a survey experiment. We build upon a theory in evolutionary psychology-disease avoidance-to predict how media coverage of the pandemic affects public support for containment measures. We report three key findings. First, exposure to a news item that shows people ignoring social distancing rules causes an increase in support for cell phone contact tracing. Second, pre-treatment covariates such as anxiety and a belief that other people are not following the rules rank among the strongest predictors of support for COVID-19 apps. And third, while a majority of respondents approve of the reliance on cell phone contact tracing, concerns for rights and freedoms remain a salient preoccupation.

COVID-19 contact tracing applications have been deployed at a fast pace around the world and may be a key policy instrument to contain future waves in Canada. This study aims to explain public opinion toward cell phone contact tracing using a survey experiment conducted with a representative sample of Canadian respondents. We build upon an established theory in evolutionary psychology—disease avoidance—to predict how media coverage of the pandemic affects public support for containment measures. We report three key findings. First, exposure to a news item that shows people ignoring social distancing rules causes an increase in support for cell phone contact tracing. Second, pre-treatment covariates such as anxiety and a belief that other people are not following the rules rank among the strongest predictors of support for COVID-19 apps. And third, while a majority of respondents approve the reliance on cell phone contact tracing, many of them hold ambivalent thoughts about the technology. Our analysis of answers to an open-ended question on the topic suggests that concerns for rights and freedoms remain a salient preoccupation.

People are commonly expected not to waste their vote on parties with small probabilities of being elected. Yet, many end up voting for underdogs. We argue that voters gauge the popular support for their preferred party from their social networks. When social networks function as echo chambers, a feature observed in real-life networks, voters overestimate underdogs’ chances of winning. We conduct voting experiments in which some treatment groups receive signals from a simulated network. We compare the effect of networks with a high degree of homogeneity against random networks. We find that homophilic networks increase the level of support for underdogs, which provides evidence to back up anecdotal claims that echo chambers foster the development of fringe parties.

This article investigates the puzzle of Canadian interprovincial migration. Despite the opportunities afforded by a large country like Canada, the proportion of residents moving to a new province has been in steep decline since the 1970s. A body of literature suggests that federal redistribution explains the limited incentives of residents to move across provinces, by equalizing the quality of services throughout the country. However, most of the available evidence relies on migration trends observed after the fact. This article introduces an original survey asking Canadian respondents squarely whether they have ever considered leaving their province. The findings support the idea that satisfaction with provincial services refrains intra-country migration. Moreover, the results show that views about the federation matter to explain attitudes toward migration. Canadians favorable to centralization, who tend to be younger and more educated, are more likely to consider moving to another province.

Word embeddings, the coefficients from neural network models predicting the use of words in context, have now become inescapable in applications involving natural language processing. Despite a few studies in political science, the potential of this methodology for the analysis of political texts has yet to be fully uncovered. This paper introduces models of word embeddings augmented with political metadata and trained on large-scale parliamentary corpora from Britain, Canada, and the United States. We fit these models with indicator variables of the party affiliation of members of parliament, which we refer to as party embeddings. We illustrate how these embeddings can be used to produce scaling estimates of ideological placement and other quantities of interest for political research. To validate the methodology, we assess our results against indicators from the Comparative Manifestos Project, surveys of experts, and measures based on roll-call votes. Our findings suggest that party embeddings are successful at capturing latent concepts such as ideology, and the approach provides researchers with an integrated framework for studying political language.

A seemingly inescapable feature of the digital age is that people choosing to devote their lives to politics must now be ready to face a barrage of insults and disparaging comments targeted at them through social media. This article represents an effort to document this phenomenon systematically. We implement machine learning models to predict the incivility of about 2.2 m messages addressed to Canadian politicians and US Senators on Twitter. Specifically, we test whether women in politics are more heavily targeted by online incivility, as recent media reports suggested. Our estimates indicate that roughly 15% of public messages sent to Senators can be categorized as uncivil, whereas the proportion is about four points lower in Canada. We find evidence that women are more heavily targeted by uncivil messages than men, although only among highly visible politicians.