Publications

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Conference Proceedings
Fulgoni, Dean, Jordan Carpenter, Lyle Ungar, and Daniel Preoţiuc-Pietro. An Empirical Exploration of Moral Foundations Theory in Partisan News Sources In LREC., 2016. AbstractPDFPoster

News sources frame issues in different ways in order to appeal or control the perception of their readers. We present a large scale study of news articles from partisan sources in the US across a variety of different issues. We first highlight that differences between sides exist by predicting the political leaning of articles of unseen political bias. Framing can be driven by different types of morality that each group values. We emphasize differences in framing of different news building on the moral foundations theory quantified using hand crafted lexicons. Our results show that partisan sources frame political issues differently both in terms of words usage and through the moral foundations they relate to.

Flekova, Lucie, Lyle Ungar, and Daniel Preoţiuc-Pietro. Exploring Stylistic Variation with Age and Income on Twitter. ACL., 2016. AbstractPDFSlides

Writing style allows NLP tools to adjust to the traits of an author. In this paper, we explore the relation between stylistic and syntactic features and authors’ age and income. We confirm our hypothesis that for numerous feature types writing style is predictive of income even beyond age. We analyze the predictive power of writing style features in a regression task on two data sets of around 5,000 Twitter users each. Additionally, we use our validated features to study daily variations in writing style of users from distinct income groups. Temporal stylistic patterns not only provide novel psychological insight into user behavior, but are useful for future research and applications in social media.

Preoţiuc-Pietro, Daniel, Justin Cranshaw, and Tae Yano. Exploring venue-based city-to-city similarity measures In Workshop on Urban Computing (UrbComp). SIGKDD., 2013. AbstractPDF

In this work we explore the use of incidentally generated social network data for the folksonomic characterization of cities by the types of amenities located within them. Using data collected about venue categories in various cities, we examine the effect of different granularities of spatial aggregation and data normalization when representing a city as a collection of its venues. We introduce three vector-based representations of a city, where aggregations of the venue categories are done within a grid structure, within the city’s municipal neighborhoods, and across the city as a whole. We apply our methods to a novel dataset consisting of Foursquare venue data from 17 cities across the United States, totaling over 1 million venues. Our preliminary investigation demonstrates that different assumptions in the urban perception could lead to qualitative, yet distinctive, variations in the induced city description and categorization.

Lampos, Vasileios, Daniel Preoţiuc-Pietro, Sina Samangooei, Douwe Gelling, and Trevor Cohn. Extracting socioeconomic patterns from the news: Modelling text and outlet importance jointly In Workshop on Language Technologies and Computational Social Science (LACSS). ACL., 2014. AbstractPDFPoster

Information from news articles can be used to study correlations between textual discourse and socioeconomic patterns. This work focuses on the task of understanding how words contained in the news as well as the news outlets themselves may relate to a set of indicators, such as economic sentiment or unemployment rates. The bilinear nature of the applied regression model facilitates learning jointly word and outlet importance, supervised by these indicators. By evaluating the predictive ability of the extracted features, we can also assess their relevance to the target socioeconomic phenomena. Therefore, our approach can be formulated as a potential NLP tool, particularly suitable to the computational social science community, as it can be used to interpret connections between vast amounts of textual content and measurable society driven factors.