Steven R Wilson

Steven R Wilson
Oakland University · Department of Computer Science and Engineering

PhD in Computer Science and Engineering

About

33
Publications
6,798
Reads
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216
Citations
Citations since 2016
32 Research Items
216 Citations
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20162017201820192020202120220102030405060
20162017201820192020202120220102030405060
20162017201820192020202120220102030405060
Education
September 2013 - January 2019
University of Michigan
Field of study
  • Computer Science
September 2013 - May 2015
University of Michigan
Field of study
  • Computer Science
September 2009 - May 2013
Taylor University
Field of study
  • Computer Science/Systems

Publications

Publications (33)
Article
Full-text available
People's values provide a decision-making framework that helps guide their everyday actions. Most popular methods of assessing values show tenuous relationships with everyday behaviors. Using a new Amazon Mechanical Turk dataset (N = 767) consisting of people's language, values, and behaviors , we explore the degree to which attaining " ground trut...
Conference Paper
Full-text available
We present a methodology based on topic modeling that can be used to identify and quantify sociolinguistic differences between groups of people, and describe a regression method that can disentangle the influences of different attributes of the people in the group (e.g., culture, gender, age). As an example, we explore the concept of personal value...
Conference Paper
Full-text available
The things people do in their daily lives can provide valuable insights into their personality, values, and interests. Unstruc-tured text data on social media platforms are rich in behavioral content, and automated systems can be deployed to learn about human activity on a broad scale if these systems are able to reason about the content of interes...
Article
Crossword puzzles are popular word games that require not only a large vocabulary, but also a broad knowledge of topics. Answering each clue is a natural language task on its own as many clues contain nuances, puns, or counter-intuitive word definitions. Additionally, it can be extremely difficult to ascertain definitive answers without the constra...
Preprint
Full-text available
Computational humor detection systems rarely model the subjectivity of humor responses, or consider alternative reactions to humor - namely offense. We analyzed a large dataset of humor and offense ratings by male and female annotators of different age groups. We find that women link these two concepts more strongly than men, and they tend to give...
Conference Paper
Full-text available
iSarcasmEval is the first shared task to target intended sarcasm detection: the data for this task was provided and labelled by the authors of the texts themselves. Such an approach min-imises the downfalls of other methods to collect sarcasm data, which rely on distant supervision or third-party annotations. The shared task contains two languages,...
Conference Paper
Narratives have been shown to be an effective way to communicate health risks and promote health behavior change, and given the growing amount of health information being shared on social media, it is crucial to study health- related narratives in social media. However, expert identification of a large number of narrative texts is a time consuming...
Preprint
Full-text available
As an online, crowd-sourced, open English-language slang dictionary, the Urban Dictionary platform contains a wealth of opinions, jokes, and definitions of terms, phrases, acronyms, and more. However, it is unclear exactly how activity on this platform relates to larger conversations happening elsewhere on the web, such as discussions on larger, mo...
Conference Paper
Full-text available
The choice of the corpus on which word embeddings are trained can have a sizable effect on the learned representations, the types of analyses that can be performed with them, and their utility as features for machine learning models. To contribute to the existing sets of pre-trained word embeddings, we introduce and release the first set of word em...
Chapter
There are several standard methods used to measure personal values, including the Schwartz Values Survey and the World Values Survey. While these tools are based on well-established questionnaires, they are expensive to administer at a large scale and rely on respondents to self-report their values rather than observing what people actually choose...
Article
Full-text available
Events and situations unfold quickly in our modern world, generating streams of Internet articles, photos, and videos. The ability to automatically sort through this wealth of information would allow us to identify which pieces of information are most important and credible, and how trends unfold over time. In this paper, we present the first piece...
Preprint
The activities we do are linked to our interests, personality, political preferences, and decisions we make about the future. In this paper, we explore the task of predicting human activities from user-generated content. We collect a dataset containing instances of social media users writing about a range of everyday activities. We then use a state...
Article
Full-text available
Understanding current world events in real-time involves sifting through news articles, tweets, photos, and videos from many different perspectives. The goal of the DARPA-funded AIDA project is to automate much of this process, building a knowledge base that can be queried to strategically generate hypotheses about different aspects of an event. We...
Chapter
We introduce a crowd-powered approach for the creation of a lexicon for any theme given a set of seed words that cover a variety of concepts within the theme. Terms are initially sorted by automatically clustering their embeddings and subsequently rearranged by crowd workers in order to create a tree structure. This type of organization captures hi...
Chapter
Previous work has investigated the identification of mental health issues in social media users, yet the way that users’ mental states and related behavior change over time remains relatively understudied. This paper focuses on online mental health communities and studies how users’ contributions to these communities change over one year. We define...
Preprint
The semantic relations between two short texts can be defined in multiple ways. Yet, all the systems to date designed to capture such relations target one relation at a time. We propose a novel multi-label transfer learning approach to jointly learn the information provided by the multiple annotations, rather than treating them as separate tasks. N...
Article
We study the problem of adapting neural sentence embedding models to the domain of human activities to capture their relations in different dimensions. We introduce a novel approach, Sequential Network Transfer, and show that it largely improves the performance on all dimensions. We also extend this approach to other semantic similarity datasets, a...
Article
People's personality and motivations are manifest in their everyday language usage. With the emergence of social media, ample examples of such usage are procurable. In this paper, we aim to analyze the vocabulary used by close to 200,000 Blogger users in the U.S. with the purpose of geographically portraying various demographic, linguistic, and psy...
Conference Paper
Full-text available
Texts posted on the web by users from diverse cultures provide a nearly endless source of data that researchers can use to study human thoughts and language patterns. However, unless care is taken to avoid it, models may be developed in one cultural setting and deployed in another, leading to unforeseen consequences. We explore the effects of using...

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