Statistical Inferences for Polarity Identification in Natural Language

Information forms the basis for all human behavior, including the ubiquitous decision-making that people constantly perform in their every day lives. It is thus the mission of researchers to understand how humans process information to reach decisions. In order to facilitate this task, this work proposes a novel method of studying the reception of granular expressions in natural language. The approach utilizes LASSO regularization as a statistical tool to extract decisive words from textual content and draw statistical inferences based on the correspondence between the occurrences of words and an exogenous response variable. Accordingly, the method immediately suggests significant implications for social sciences and Information Systems research: everyone can now identify text segments and word choices that are statistically relevant to authors or readers and, based on this knowledge, test hypotheses from behavioral research. We demonstrate the contribution of our method by examining how authors communicate subjective information through narrative materials. This allows us to answer the question of which words to choose when communicating negative information. On the other hand, we show that investors trade not only upon facts in financial disclosures but are distracted by filler words and non-informative language. Practitioners - for example those in the fields of investor communications or marketing - can exploit our insights to enhance their writings based on the true perception of word choice

Unfolding political attitudes through the face: facial expressions when reading emotion language of left- and right-wing political leaders

Spontaneous emotionally congruent facial responses (ECFR) to others\u2019 emotional expressions can occur by simply observing others\u2019 faces (i.e., smiling) or by reading emotion related words (i.e., to smile). The goal of the present study was to examine whether language describing political leaders\u2019 emotions affects voters by inducing emotionally congruent facial reactions as a function of readers\u2019 and politicians\u2019 shared political orientation. Participants read sentences describing politicians\u2019 emotional expressions, while their facial muscle activation was measured by means of electromyography (EMG). Results showed that reading sentences describing left and right-wing politicians \u201csmiling\u201d or \u201cfrowning\u201d elicits ECFR for ingroup but not outgroup members. Remarkably, ECFR were sensitive to attitudes toward individual leaders beyond the ingroup vs. outgroup political divide. Through integrating behavioral and physiological methods we were able to consistently tap on a \u2018favored political leader effect\u2019 thus capturing political attitudes towards an individual politician at a given moment of time, at multiple levels (explicit responses and automatic ECFR) and across political party membership lines. Our findings highlight the role of verbal behavior of politicians in affecting voters\u2019 facial expressions with important implications for social judgment and behavioral outcomes

Persistence of Cultural Heritage in a Multicultural Context: Examining Factors that Shaped Voting Preferences in the 2016 Election

The prevailing discourse about the myth of the “melting pot” of American culture implies that heritage cultures are eliminated in favor of a homogenous “American” norm. However, this myth belies the persistence of our cultural heritage in forming our attitudes, morals, and habitual patterns of thought, each of which shape how we participate in our democracy through voting. By contextualizing voting predictors such as authoritarianism, social dominance, and sexism in developmental and ecological theories, this dissertation shows how they are shaped by culture and transmitted through consumption of media and interaction with members of one’s community and family. In an effort to model voting preferences using psychological constructs rather than demographic proxies such as race, gender or age, political scientists Feldman and Stenner (1997) have identified authoritarian parenting attitudes as a key parameter that predicts voting preferences for conservative candidates. Other scholars have identified additional parameters, such as hostile sexism (Glick and Fiske, 1996) and social dominance orientation (Pratto, Sidanius, Stallworth & Malle, 1994) while scholars such as Graham et al. (2011) have drawn together these separate predictors into a comprehensive, multidimensional measure of political ideology situated in the literature on moral development, yet scholars have neglected the role of culture in shaping our voting preferences and the psychological constructs which underlie and drive them. While psychological constructs pinpoint the mechanisms for people’s voting behavior rather than essentializing behavior to demographic groups, most of the literature on voting preferences categorizes the predictors as personality or individual difference variables, or not at all. Integrating three theories on cultural ecology (Bronfenbrenner, Greenfield and Hofstede) with Tajfel and Turner’s Social Identity Theory, this dissertation seeks to open a dialogue about the tensions between individual differences variables and cultural variables, and how they both contribute to shaping outcome behaviors such as taking a moral stance and then voting in accordance with it. This work assembles the threads from recent research to create a model which predicts voting decisions, contextualized in a multicultural environment, to tease out the role of culture as a contributor. Using an extensive online survey, we replicated findings from prior literature which indicated that hostile sexism (but not being a man), authoritarian parenting attitudes, and a social dominance orientation predicted voting preferences for Donald Trump compared to Hillary Clinton. A new predictor, heritage-culture individualism, was developed for this dissertation and significantly predicted participants’ preference for Donald Trump. Given ongoing debate in cross-cultural psychology about the degree to which culture can be studied as an individual difference or as characteristic of one’s heritage countries, we compared individual difference measures of cultural values with the mean cultural value orientation of one’s heritage country or countries. Findings suggest that the impact of heritage cultures, or the values, norms, and rules brought by our ancestors from our heritage countries and regions, is a significant component that shapes voting decisions while individual difference cultural variables are less predictive. Taken together and situated in theoretical perspectives, these findings suggest that voting preferences are shaped by cultural values, and prompts scholars to recast previous predictors, such as authoritarianism, as having a larger component of culture than previously acknowledged. This novel finding speaks to a broader debate in cross-cultural psychology by providing support for Hofstede’s assertion that cultural values represent coherent wholes that are more than the sum of the values of the people comprising them. It suggests a model which combines elements of Hofstede’s, Greenfield’s, and Bronfenbrenner’s theories of cultural ecology. With a better understanding of where identities, values, and ideas come from, we believe that interventions aimed at persuading voters can be more pluralistically sensitive to different ideologies while still increasing awareness of social justice issues

Social Media and Electoral Predictions: A Meta-Analytic Review

Can social media data be used to make reasonably accurate estimates of electoral outcomes? We conducted a meta-analytic review to examine the predictive performance of different features of social media posts and different methods in predicting political elections: (1) content features; and (2) structural features. Across 45 published studies, we find significant variance in the quality of predictions, which on average still lag behind those in traditional survey research. More specifically, our findings that machine learning-based approaches generally outperform lexicon-based analyses, while combining structural and content features yields most accurate predictions

A Gaze into politics. The role of ideology, personality and political group processing in shaping automatic social behaviors

Studies in human and non-human primates indicate that basic socio-cognitive operations are inherently linked to the power of gaze in capturing reflexively the attention of an observer. Here I report a series of behavioral and neural investigation studies that I and my collaborators have conducted on the modulation of this automatic social behavior by high order factors as politics. In particular, we showed that Gaze following behavior is permeable to social identities within the political domain, individual differences in ideology and personality and low level facial features that drive our inferences on the personality of a character. Furthermore I discussed which are the social processes that underly this basic social cognitive behavior and sketched future directions to better clarify this issue

Predicting Precedent: A Psycholinguistic Artificial Intelligence in the Supreme Court

Since the proliferation of analytic methodologies and ‘big data’ in the 1980s, there have been multiple studies claiming to offer consistent predictions for Supreme Court behavior. Political scientists focus on analyzing the ideology of judges, with prediction accuracy as high as 70%. Institutionalists, such as Kaufmann (2019), seek to make predictions on verdicts based on a thorough, qualitative analysis of rules and structures, with predictive accuracy as high as 75%. We argue that a psycholinguistic model utilizing machine learning (SCOTUS_AI) can best predict Court outcomes. Extracting sentiment features from parsed briefs through the Linguistic Inquiry and Word Count (LIWC), our results indicate SCOTUS_AI (AUC = .8087; Top K=.9144) outcompetes traditional analysis in both class-controlled accuracy and range of possible, specific outcomes. Moreover, unlike traditional models, SCOTUS_AI can also predict the procedural outcome of the case as one-hot encoded by remand (AUC=.76). Our findings support a psycholinguistic paradigm of case analysis, suggesting that the framing of arguments is a relatively strong predictor of case results. Finally, we cast predictions for the Supreme Court docket, demonstrating that SCOTUS_AI can be practically deployed in the field for individual cases

Experimentally exploring how the awareness of existential freedom influences support for autocratic leadership styles among individuals high and low in neuroticism

"May 2014."Dissertation Supervisor: Dr. Jamie Arndt.Includes vita.The present dissertation builds on classic existential philosophy and psychological theory to suggest that personal freedom can be burdensome to the self and may, ironically, motivate people to displace their freedom to an autocratic (vs. democratic) authority who would make decisions on behalf of such individuals rather than democratically involve them in the decision-making process. The present analysis further suggests that low neurotics are especially likely to actively "escape" their freedom by displacing it, whereas high neurotics instead employ inactive strategies and are unlikely to actively displace their freedom. Three preliminary studies explore and demonstrate these processes. A fourth study then proposes that the process of displacing personal responsibility for one's freedom is an important part of maintaining psychological equanimity, and offers an experiment designed to test whether displacement to autocratic authority helps reduce anxiety among low neurotics perceiving increased personal freedom. Results of this study did not support the hypothesis: displacement did not relieve explicit anxiety--reported anxiety was greater among low neurotics reminded of freedom whether or not they were first allowed to displace to authority. Instead, the displacement effect emerged when leadership style was measured first (replicating the preliminary studies), yet was eliminated when measured after participants reported on their explicit anxiety. The implications of these findings are considered in terms of alternative explanations, theoretical refinements, and future research effort.Includes bibliographical references (pages 52-61)

Affect-based information retrieval

One of the main challenges Information Retrieval (IR) systems face nowadays originates from the semantic gap problem: the semantic difference between a user’s query representation and the internal representation of an information item in a collection. The gap is further widened when the user is driven by an ill-defined information need, often the result of an anomaly in his/her current state of knowledge. The formulated search queries, which are submitted to the retrieval systems to locate relevant items, produce poor results that do not address the users’ information needs. To deal with information need uncertainty IR systems have employed in the past a range of feedback techniques, which vary from explicit to implicit. The first category of feedback techniques necessitates the communication of explicit relevance judgments, in return for better query reformulations and recommendations of relevant results. However, the latter happens at the expense of users’ cognitive resources and, furthermore, introduces an additional layer of complexity to the search process. On the other hand, implicit feedback techniques make inferences on what is relevant based on observations of user search behaviour. By doing so, they disengage users from the cognitive burden of document rating and relevance assessments. However, both categories of RF techniques determine topical relevance with respect to the cognitive and situational levels of interaction, failing to acknowledge the importance of emotions in cognition and decision making. In this thesis I investigate the role of emotions in the information seeking process and develop affective feedback techniques for interactive IR. This novel feedback framework aims to aid the search process and facilitate a more natural and meaningful interaction. I develop affective models that determine topical relevance based on information gathered from various sensory channels, and enhance their performance using personalisation techniques. Furthermore, I present an operational video retrieval system that employs affective feedback to enrich user profiles and offers meaningful recommendations of unseen videos. The use of affective feedback as a surrogate for the information need is formalised as the Affective Model of Browsing. This is a cognitive model that motivates the use of evidence extracted from the psycho-somatic mobilisation that occurs during cognitive appraisal. Finally, I address some of the ethical and privacy issues that arise from the social-emotional interaction between users and computer systems. This study involves questionnaire data gathered over three user studies, from 74 participants of different educational background, ethnicity and search experience. The results show that affective feedback is a promising area of research and it can improve many aspects of the information seeking process, such as indexing, ranking and recommendation. Eventually, it may be that relevance inferences obtained from affective models will provide a more robust and personalised form of feedback, which will allow us to deal more effectively with issues such as the semantic gap