Two-layer classification and distinguished representations of users and documents for grouping and authorship identification

Most studies on authorship identification reported a drop in the identification result when the number of authors exceeds 20-25. In this paper, we introduce a new user representation to address this problem and split classification across two layers. There are at least 3 novelties in this paper. First, the two-layer approach allows applying authorship identification over larger number of authors (tested over 100 authors), and it is extendable. The authors are divided into groups that contain smaller number of authors. Given an anonymous document, the primary layer detects the group to which the document belongs. Then, the secondary layer determines the particular author inside the selected group. In order to extract the groups linking similar authors, clustering is applied over users rather than documents. Hence, the second novelty of this paper is introducing a new user representation that is different from document representation. Without the proposed user representation, the clustering over documents will result in documents of author(s) distributed over several clusters, instead of a single cluster membership for each author. Third, the extracted clusters are descriptive and meaningful of their users as the dimensions have psychological backgrounds. For authorship identification, the documents are labelled with the extracted groups and fed into machine learning to build classification models that predicts the group and author of a given document. The results show that the documents are highly correlated with the extracted corresponding groups, and the proposed model can be accurately trained to determine the group and the author identity

When Do People Trust Their Social Groups?

Trust facilitates cooperation and supports positive outcomes in social groups, including member satisfaction, information sharing, and task performance. Extensive prior research has examined individuals' general propensity to trust, as well as the factors that contribute to their trust in specific groups. Here, we build on past work to present a comprehensive framework for predicting trust in groups. By surveying 6,383 Facebook Groups users about their trust attitudes and examining aggregated behavioral and demographic data for these individuals, we show that (1) an individual's propensity to trust is associated with how they trust their groups, (2) smaller, closed, older, more exclusive, or more homogeneous groups are trusted more, and (3) a group's overall friendship-network structure and an individual's position within that structure can also predict trust. Last, we demonstrate how group trust predicts outcomes at both individual and group level such as the formation of new friendship ties.Comment: CHI 201

Debbie, the Debate Bot of the Future

Chatbots are a rapidly expanding application of dialogue systems with companies switching to bot services for customer support, and new applications for users interested in casual conversation. One style of casual conversation is argument, many people love nothing more than a good argument. Moreover, there are a number of existing corpora of argumentative dialogues, annotated for agreement and disagreement, stance, sarcasm and argument quality. This paper introduces Debbie, a novel arguing bot, that selects arguments from conversational corpora, and aims to use them appropriately in context. We present an initial working prototype of Debbie, with some preliminary evaluation and describe future work.Comment: IWSDS 201

Typical Phone Use Habits: Intense Use Does Not Predict Negative Well-Being

Not all smartphone owners use their device in the same way. In this work, we uncover broad, latent patterns of mobile phone use behavior. We conducted a study where, via a dedicated logging app, we collected daily mobile phone activity data from a sample of 340 participants for a period of four weeks. Through an unsupervised learning approach and a methodologically rigorous analysis, we reveal five generic phone use profiles which describe at least 10% of the participants each: limited use, business use, power use, and personality- & externally induced problematic use. We provide evidence that intense mobile phone use alone does not predict negative well-being. Instead, our approach automatically revealed two groups with tendencies for lower well-being, which are characterized by nightly phone use sessions.Comment: 10 pages, 6 figures, conference pape

How does threat affect different types of people? Investigating a relationship between Big-Five personality and self-concept, and how threat may affect a self-concept network

In this exploratory project, we aim to draw connections between Big-5 personality, threat, and self-concept. In the experiment, participants first completed the Big-5 inventory of personality measuring openness, conscientiousness, extraversion, agreeableness, and neuroticism, five mostly independent traits that form a broad picture of personality (John et al., 2008; John et al., 1991; Benet-Martinez & John, 1998). Next, participants were randomly assigned to a threat or non-threat condition. Threat was manipulated using mortality salience, a prompt in which participants were asked to write specifically about their bodily reaction to death, a domain non-specific threat (Rosenblatt et al., 1989). After the threat manipulation, self-concept measures were administered. Self-concept has been operationalized here as a network, adapting research on social networks to a self- and identity-based model. The self-concept network is created by having participants list 15 personal identities, rate each identity's importance, and then determine how related each identity is to the others. Similar to a social network, clusters emerge that determine which identities are most important to the self. During data coding, each of these identities were rated by two judges blind to condition as either agentic or communal. No significant results were found for threat as a main effect and for personality as a moderator of the relationship between threat and identities. However, people significantly listed more agentic identities than communal, and more agentic identities and higher agentic importance were marginally correlated with higher self-concept clarity and positive affect, possibly suggesting more comfort in understanding more self-focused identities. There was also a marginally significant increase in perceived importance of all identities and marginally significant increase in agentic identities after threat. In future research, we would like to replicate this research with more participants, different threat manipulations, more focused independent variables, and also explore differences in how people rate their own identities.No embargoAcademic Major: Psycholog