Relative group size and minority school success: the role of intergroup friendship and discrimination experiences

From an intergroup relations perspective, relative group size is associated with the quantity and quality of intergroup contact: more positive contact (i.e., intergroup friendship) supports, and negative contact (i.e., experienced discrimination) hampers, minority identity, and school success. Accordingly, we examined intergroup contact as the process through which perceived relative proportions of minority and majority students in school affected minority success (i.e., school performance, satisfaction, and self-efficacy). Turkish minorities (N = 1,060) were compared in four Austrian and Belgian cities which differ in their typical school ethnic composition. Across cities, minority experiences of intergroup contact fully mediated the impact of perceived relative group size on school success. As expected, higher minority presence impaired school success through restricting intergroup friendship and increasing experienced discrimination. The association between minority presence and discrimination was curvilinear, however, so that schools where minority students predominated offered some protection from discrimination. To conclude, the comparative findings reveal positive and negative intergroup contact as key processes that jointly explain when and how higher proportions of minority students affect school success

THE INFLUENCE OF DEPRESSIVE SYMPTOMS ON FATHERS’ BEHAVIORS AND ATTITUDES

The present study examines the impact of young, poor, unwed fathers on their family by investigating the influence of depressive symptom frequency on fathers’ relationship with their children and partners. Couples from seven American cities with populations over 200,000 were recruited and interviewed about such areas of life as romantic and parental relationships, health, and employment at the hospital within 24 hours after the birth of their child. An Item Response Theory (IRT) within-group analysis of the 1,759 African-, Caucasian-, Hispanic-, Asian-, and Native American fathers in the study yielded a 3-class clustering of depressive symptoms. Class 1 fathers had the lowest frequency of depressive symptom expression; class 2 fathers had a low frequency; and class 3 fathers had low to medium rates of depressive symptoms. Multivariate statistics revealed that depressive class membership predicted domestic violence toward fathers’ partners but not affection toward their children. The importance of the parental behavior of teaching children about life, however, varied by class, with class 2 fathers most highly endorsing this behavior. Implications of young, unwed, poor fathers' behaviors and attitudes toward their children and romantic partners will be discussed in terms of men's contributions to family life.

Analyzing the Language of Food on Social Media

We investigate the predictive power behind the language of food on social media. We collect a corpus of over three million food-related posts from Twitter and demonstrate that many latent population characteristics can be directly predicted from this data: overweight rate, diabetes rate, political leaning, and home geographical location of authors. For all tasks, our language-based models significantly outperform the majority-class baselines. Performance is further improved with more complex natural language processing, such as topic modeling. We analyze which textual features have most predictive power for these datasets, providing insight into the connections between the language of food, geographic locale, and community characteristics. Lastly, we design and implement an online system for real-time query and visualization of the dataset. Visualization tools, such as geo-referenced heatmaps, semantics-preserving wordclouds and temporal histograms, allow us to discover more complex, global patterns mirrored in the language of food.Comment: An extended abstract of this paper will appear in IEEE Big Data 201

Diffusion of Lexical Change in Social Media

Computer-mediated communication is driving fundamental changes in the nature of written language. We investigate these changes by statistical analysis of a dataset comprising 107 million Twitter messages (authored by 2.7 million unique user accounts). Using a latent vector autoregressive model to aggregate across thousands of words, we identify high-level patterns in diffusion of linguistic change over the United States. Our model is robust to unpredictable changes in Twitter's sampling rate, and provides a probabilistic characterization of the relationship of macro-scale linguistic influence to a set of demographic and geographic predictors. The results of this analysis offer support for prior arguments that focus on geographical proximity and population size. However, demographic similarity -- especially with regard to race -- plays an even more central role, as cities with similar racial demographics are far more likely to share linguistic influence. Rather than moving towards a single unified "netspeak" dialect, language evolution in computer-mediated communication reproduces existing fault lines in spoken American English.Comment: preprint of PLOS-ONE paper from November 2014; PLoS ONE 9(11) e11311

Describing and Understanding Neighborhood Characteristics through Online Social Media

Geotagged data can be used to describe regions in the world and discover local themes. However, not all data produced within a region is necessarily specifically descriptive of that area. To surface the content that is characteristic for a region, we present the geographical hierarchy model (GHM), a probabilistic model based on the assumption that data observed in a region is a random mixture of content that pertains to different levels of a hierarchy. We apply the GHM to a dataset of 8 million Flickr photos in order to discriminate between content (i.e., tags) that specifically characterizes a region (e.g., neighborhood) and content that characterizes surrounding areas or more general themes. Knowledge of the discriminative and non-discriminative terms used throughout the hierarchy enables us to quantify the uniqueness of a given region and to compare similar but distant regions. Our evaluation demonstrates that our model improves upon traditional Naive Bayes classification by 47% and hierarchical TF-IDF by 27%. We further highlight the differences and commonalities with human reasoning about what is locally characteristic for a neighborhood, distilled from ten interviews and a survey that covered themes such as time, events, and prior regional knowledgeComment: Accepted in WWW 2015, 2015, Florence, Ital

What’s Behind Recent Transit Ridership Trends in the Bay Area? Volume I: Overview and Analysis of Underlying Factors

Public transit ridership has been falling nationally and in California since 2014. The San Francisco Bay Area, with the state’s highest rates of transit use, had until recently resisted those trends, especially compared to Greater Los Angeles. However, in 2017 and 2018 the region lost over five percent (>27 million) of its annual riders, despite a booming economy and service increases. This report examines Bay Area transit ridership to understand the dimensions of changing transit use, its possible causes, and potential solutions. We find that: 1) the steepest ridership losses have come on buses, at off-peak times, on weekends, in non-commute directions, on outlying lines, and on operators that do not serve the region’s core employment clusters; 2) transit trips in the region are increasingly commute-focused, particularly into and out of downtown San Francisco; 3) transit commuters are increasingly non-traditional transit users, such as those with higher incomes and automobile access; 4) the growing job-housing imbalance in the Bay Area is related to rising housing costs and likely depressing transit ridership as more residents live less transit-friendly parts of the region; and 5) ridehail is substituting for some transit trips, particularly in the off-peak. Arresting falling transit use will likely require action both by transit operators (to address peak capacity constraints; improve off-peak service; ease fare payments; adopt fare structures that attract off-peak riders; and better integrate transit with new mobility options) and public policymakers in other realms (to better meter and manage private vehicle use and to increase the supply and affordability of housing near job centers)