A Quantitative Discourse Analysis of Asian Workers in the US Historical Newspapers

Warning: This paper contains examples of offensive language targetting marginalized population. The digitization of historical texts invites researchers to explore the large-scale corpus of historical texts with computational methods. In this study, we present computational text analysis on a relatively understudied topic of how Asian workers are represented in historical newspapers in the United States. We found that the word "coolie" was semantically different in some States (e.g., Massachusetts, Rhode Island, Wyoming, Oklahoma, and Arkansas) with the different discourses around coolie. We also found that then-Confederate newspapers and then-Union newspapers formed distinctive discourses by measuring over-represented words. Newspapers from then-Confederate States associated coolie with slavery-related words. In addition, we found Asians were perceived to be inferior to European immigrants and subjected to the target of racism. This study contributes to supplementing the qualitative analysis of racism in the United States with quantitative discourse analysis.Comment: 3rd International Conference on Natural Language Processing for Digital Humanities (NLP4DH

Cross-platform Analysis of Twitter and Parler during the 2020 U.S. Presidential Election

In the recent 2020 Presidential Election, President Trump and his campaign alleged that mail-in ballots were likely to be fraudulent and this claim stood against Twitter’s efforts to curb spreading of misinformation (Lima, 2020). This claim resulted in suspending those who participated in voter fraud misinformation (Twitter, 2021), including Trump himself. In response to Twitter’s action, Trump and those who supported Trump left Twitter, seeking an alternative social media. This migration was a strong collective action by users who felt more than simply constrained (Kiene, Monroy-Hernández & Hill, 2016) by a loss of belonging to the community when users faced increased censorship. Those who left Twitter found Parler as an alternative social networking service, which proclaims that they allow a user to “speak freely and express yourself openly, without fear of being deplatformed for your views” as an asylum. Parler has gained attention from conservatives who are looking for alternative social media, which supposedly accepts them for who they are. Based on this unique case, this study seeks to understand the impact of echo chambers on people’s expressed opinions on social media. Past research efforts on echo chambers, selective exposure, and network homogeneity (Stewart, Arif & Starbird, 2018; Jacobson, Myung & Johnson, 2016) mostly focused on a handful of popular social media, mostly either Facebook or Twitter, while neglecting the unique roles of other niche social media platforms in building online communities (Zannettou et al., 2018). We will address this critical gap by leveraging data from two social media platforms: Parler and Twitter as examples that represent distinctive user bases in terms of political ideology. We identify users who have the same account names on both platforms and examine the role of political homogeneity in the online opinion expression and sharing of information. We rely on the Social Identity Deindividuation Effects (SIDE) model to understand political behaviors of the users who used both Twitter and Parler. The SIDE model explains that deindividualization occurs when group norms are more salient and have a greater effect on individual behaviors than individual processes (Lea & Spears, 1992). The SIDE models focus on anonymity and explicit and implicit norms of online spaces, and supports that anonymity enhances the social influence processes and collective behavior (Spears, 2017). By applying this theoretical model, we are aiming to reveal how Parler’s homogeneous political climate – more conservative than Twitter – helped users to feel more anonymous than Twitter by providing a safe place for them to speak hatred. There are two research questions we wanted to answer. Our focus of interest is the people who used both Twitter and Parler and hereafter, they are called cross-platform users. RQ 1. Can we make use of the machine learning technique to identify the pattern of increasing or decreasing use of toxic language by cross-platform users in Twitter? RQ 2. Can we make use of the machine learning technique to identify the pattern of increasing or decreasing use of toxic language by cross-platform users in Parler?Ope

Overweight People Have Low Levels of Implicit Weight Bias, but Overweight Nations Have High Levels of Implicit Weight Bias

Although a greater degree of personal obesity is associated with weaker negativity toward overweight people on both explicit (i.e., self-report) and implicit (i.e., indirect behavioral) measures, overweight people still prefer thin people on average. We investigated whether the national and cultural context - particularly the national prevalence of obesity predicts attitudes toward overweight people independent of personal identity and weight status. Data were collected from a total sample of 338,121 citizens from 71 nations in 22 different languages on the Project Implicit website (https://implicit.harvard.edu/) between May 2006 and October 2010. We investigated the relationship of the explicit and implicit weight bias with the obesity both at the individual (i.e., across individuals) and national (i.e., across nations) level. Explicit weight bias was assessed with self-reported preference between overweight and thin people; implicit weight bias was measured with the Implicit Association Test (IAT). The national estimates of explicit and implicit weight bias were obtained by averaging the individual scores for each nation. Obesity at the individual level was defined as Body Mass Index (BMI) scores, whereas obesity at the national level was defined as three national weight indicators (national BMI, national percentage of overweight and underweight people) obtained from publicly available databases. Across individuals, greater degree of obesity was associated with weaker implicit negativity toward overweight people compared to thin people. Across nations, in contrast, a greater degree of national obesity was associated with stronger implicit negativity toward overweight people compared to thin people. This result indicates a different relationship between obesity and implicit weight bias at the individual and national levels

Adaptive Weighted Multi-Discriminator CycleGAN for Underwater Image Enhancement

In this paper, we propose a novel underwater image enhancement method. Typical deep learning models for underwater image enhancement are trained by paired synthetic dataset. Therefore, these models are mostly effective for synthetic image enhancement but less so for real-world images. In contrast, cycle-consistent generative adversarial networks (CycleGAN) can be trained with unpaired dataset. However, performance of the CycleGAN is highly dependent upon the dataset, thus it may generate unrealistic images with less content information than original images. A novel solution we propose here is by starting with a CycleGAN, we add a pair of discriminators to preserve contents of input image while enhancing the image. As a part of the solution, we introduce an adaptive weighting method for limiting losses of the two types of discriminators to balance their influence and stabilize the training procedure. Extensive experiments demonstrate that the proposed method significantly outperforms the state-of-the-art methods on real-world underwater images

Remodeling Archival Metadata Descriptions for Linked Archives

Though archival resources may be valued for their uniqueness, they do not exist in isolation from each other, and stand to benefit from linked data treatments capable of exposing them to a wider network of resources and potential users. To leverage these benefits, existing, item-level metadata depicting physical materials and their digitized surrogates must be remodeled as linked data. A number of solutions exist, but many current models in this domain are complex and may not capture all relevant aspects of larger, heterogeneous collections of media materials. This paper presents the development of the Linked Archives model, a linked data approach to making item-level metadata available for archival collections of media materials, including photographs, sound recordings, and video recordings. Developed and refined through an examination of existing collection and item metadata alongside comparisons to established domain ontologies and vocabularies, this model takes a modular approach to remodeling archival data as linked data. Current efforts focused on a simplified, user discovery focused module intended to improve access to these materials and the incorporation of their metadata into the wider web of data. This project contributes to work exploring the representation of the range of archival and special collections and how these materials may be addressed via linked data models

Overweight People Have Low Levels of Implicit Weight Bias, but Overweight Nations Have High Levels of Implicit Weight Bias

Although a greater degree of personal obesity is associated with weaker negativity toward overweight people on both explicit (i.e., self-report) and implicit (i.e., indirect behavioral) measures, overweight people still prefer thin people on average. We investigated whether the national and cultural context \u2013 particularly the national prevalence of obesity \u2013 predicts attitudes toward overweight people independent of personal identity and weight status. Data were collected from a total sample of 338,121 citizens from 71 nations in 22 different languages on the Project Implicit website (https://implicit.harvard.edu/) between May 2006 and October 2010. We investigated the relationship of the explicit and implicit weight bias with the obesity both at the individual (i.e., across individuals) and national (i.e., across nations) level. Explicit weight bias was assessed with self-reported preference between overweight and thin people; implicit weight bias was measured with the Implicit Association Test (IAT). The national estimates of explicit and implicit weight bias were obtained by averaging the individual scores for each nation. Obesity at the individual level was defined as Body Mass Index (BMI) scores, whereas obesity at the national level was defined as three national weight indicators (national BMI, national percentage of overweight and underweight people) obtained from publicly available databases. Across individuals, greater degree of obesity was associated with weaker implicit negativity toward overweight people compared to thin people. Across nations, in contrast, a greater degree of national obesity was associated with stronger implicit negativity toward overweight people compared to thin people. This result indicates a different relationship between obesity and implicit weight bias at the individual and national levels

National differences in gender-science stereotypes predict national sex differences in science and math achievement

About 70% of more than half a million Implicit Association Tests completed by citizens of 34 countries revealed expected implicit stereotypes associating science with males more than with females. We discovered that nation-level implicit stereotypes predicted nation-level sex differences in 8th-grade science and mathematics achievement. Self-reported stereotypes did not provide additional predictive validity of the achievement gap. We suggest that implicit stereotypes and sex differences in science participation and performance are mutually reinforcing, contributing to the persistent gender gap in science engagement.Psycholog