Исследование влияния пола и психологических характеристик автора на количественные параметры его текста с использованием программы Linguistic Inquiry and Word Count

The article examines and analyzes the linguistic and psychological features of political discourse using a computer-based Linguistic Inquiry and Word Count (LIWC) content analysis program to explore the relationship between political discourse and the personality of politicians. As for political discourse, it is perhaps the communicator, the linguistic personality, who plays the most important role in the communication. The linguistic personality of a politician is of particular interest in political discourse content-analysis, since it has the greatest influence on the public consciousness via mass media. Using text as a source of psychological and cognitive information has been gaining popularity. Researchers use a variety of methods to analyze texts, but Linguistic Inquiry Word Count (LIWC) has proved to be the most common technique. The analysis of linguistic patterns of political discourse shows that in the context of political speech events such as media interviews, politicians make a unique choice of lexical units, which can be interpreted as a manifestation of certain personality traits. However, despite the significance of the results, there are clear limitations to the use of computerized methodologies to make political discourse content-analysis, such as the limited interpretive capacity of software to understand pragmatic and contextual use of lexical units.

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Political discourse content analysis: a critical overview of a computerized text analysis program linguistic inquiry and word count (liwc)

In the debate about filter bubbles caused by algorithmic news recommendation, the conceptualization of the two core concepts in this debate, diversity and algorithms, has received little attention in social scientific research. This paper examines the effect of multiple recommender systems on different diversity dimensions. To this end, it maps different values that diversity can serve, and a respective set of criteria that characterizes a diverse information offer in this particular conception of diversity. We make use of a data set of simulated article recommendations based on actual content of one of the major Dutch broadsheet newspapers and its users (N=21,973 articles, N=500 users). We find that all of the recommendation logics under study proved to lead to a rather diverse set of recommendations that are on par with human editors and that basing recommendations on user histories can substantially increase topic diversity within a recommendation set.

/pdf/do-not-blame-it-on-the-algorithm-an-empirical-assessment-of-2zfa2hbuqy.pdf

Do not blame it on the algorithm: an empirical assessment of multiple recommender systems and their impact on content diversity

Online abusive content detection is an inherently difficult task. It has received considerable attention from academia, particularly within the computational linguistics community, and performance appears to have improved as the field has matured. However, considerable challenges and unaddressed frontiers remain, spanning technical, social and ethical dimensions. These issues constrain the performance, efficiency and generalizability of abusive content detection systems. In this article we delineate and clarify the main challenges and frontiers in the field, critically evaluate their implications and discuss potential solutions. We also highlight ways in which social scientific insights can advance research. We discuss the lack of support given to researchers working with abusive content and provide guidelines for ethical research.

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Challenges and frontiers in abusive content detection

PROBLEM/CONDITION
Priority health risk behaviors that contribute to the leading causes of mortality, morbidity, and social problems among youth and adults often are established during youth, extend into adulthood, and are interrelated.


REPORTING PERIOD
February through May 1993.


DESCRIPTION OF SYSTEM
The Youth Risk Behavior Surveillance System (YRBSS) monitors six categories of priority health risk behaviors among youth and young adults: behaviors that contribute to unintentional and intentional injuries, tobacco use, alcohol and other drug use, sexual behaviors, dietary behaviors, and physical activity. The YRBSS includes a national, school-based survey conducted by CDC and state and local school-based surveys conducted by state and local education agencies. This report summarizes results from the national survey, 24 state surveys, and nine local surveys conducted among high school students during February through May 1993.


RESULTS AND INTERPRETATION
In the United States, 72% of all deaths among school-age youth and young adults are from four causes: motor vehicle crashes, other unintentional injuries, homicide, and suicide. Results from the 1993 YRBSS suggest that many high school students practice behaviors that may increase their likelihood of death from these four causes: 19.1% rarely or never used a safety belt, 35.3% had ridden with a driver who had been drinking alcohol during the 30 days preceding the survey, 22.1% had carried a weapon during the 30 days preceding the survey, 80.9% ever drank alcohol, 32.8% ever used marijuana, and 8.6% had attempted suicide during the 12 months preceding the survey. Substantial morbidity and social problems among adolescents also result from unintended pregnancies and sexually transmitted diseases, including human immunodeficiency virus (HIV) infection. YRBSS results indicate that in 1993, 53.0% of high school students had had sexual intercourse, 52.8% of sexually active students had used a condom during last sexual intercourse, and 1.4% ever injected an illegal drug. Among adults, 67% of all deaths are from three causes: heart disease, cancer, and stroke. In 1993, many high school students practiced behaviors that may increase the risk for these health problems: 30.5% of high school students had smoked cigarettes during the 30 days preceding the survey, only 15.4% had eaten five or more servings of fruits and vegetables during the day preceding the survey, and only 34.3% had attended physical education class daily.


ACTIONS TAKEN
YRBSS data are being used nationwide by health and education officials to improve school health policies and programs designed to reduce risks associated with the leading causes of mortality and morbidity. At the national level, YRBSS data are being used to measure progress toward achieving 26 national health objectives and one of eight National Education Goals.

Youth Risk Behavior Surveillance - United States, 1993

The suggestions proposed by Lee et al. to improve cognitive modeling practices have significant parallels to the current best practices for improving reproducibility in the field of Machine Learning. In the current commentary on `Robust modeling in cognitive science', we highlight the practices that overlap and discuss how similar proposals have produced novel ongoing challenges, including cultural change towards open science, the scalability and interpretability of required practices, and the downstream effects of having robust practices that are fully transparent. Through this, we hope to inform future practices in computational modeling work with a broader scope.

/pdf/towards-replication-in-computational-cognitive-modeling-a-wu0w8a94hb.pdf

Towards Replication in Computational Cognitive Modeling : A Machine Learning Perspective

A limited amount of studies investigates the role of model-agnostic adversarial behavior in toxic content classification. As toxicity classifiers predominantly rely on lexical cues, (deliberately) creative and evolving language-use can be detrimental to the utility of current corpora and state-of-the-art models when they are deployed for content moderation. The less training data is available, the more vulnerable models might become. This study is, to our knowledge, the first to investigate the effect of adversarial behavior and augmentation for cyberbullying detection. We demonstrate that model-agnostic lexical substitutions significantly hurt classifier performance. Moreover, when these perturbed samples are used for augmentation, we show models become robust against word-level perturbations at a slight trade-off in overall task performance. Augmentations proposed in prior work on toxicity prove to be less effective. Our results underline the need for such evaluations in online harm areas with small corpora.

Cyberbullying Classifiers are Sensitive to Model-Agnostic Perturbations

Replication Data for Automatic Detection of Cyberbullying in Social Media Text

Recent years have seen an increasing need for gender-neutral and inclusive language. Within the field of NLP, there are various mono- and bilingual use cases where gender inclusive language is appropriate, if not preferred due to ambiguity or uncertainty in terms of the gender of referents. In this work, we present a rule-based and a neural approach to gender-neutral rewriting for English along with manually curated synthetic data (WinoBias+) and natural data (OpenSubtitles and Reddit) benchmarks. A detailed manual and automatic evaluation highlights how our NeuTral Rewriter, trained on data generated by the rule-based approach, obtains word error rates (WER) below 0.18% on synthetic, in-domain and out-domain test sets.

NeuTral Rewriter: A Rule-Based and Neural Approach to Automatic Rewriting into Gender-Neutral Alternatives

/pdf/cyberbullying-detection-on-social-media-3b4c3fw2.pdf

Chris Emmery

Papers

Towards Replication in Computational Cognitive Modeling : A Machine Learning Perspective

Cyberbullying Classifiers are Sensitive to Model-Agnostic Perturbations

Replication Data for Automatic Detection of Cyberbullying in Social Media Text

NeuTral Rewriter: A Rule-Based and Neural Approach to Automatic Rewriting into Gender-Neutral Alternatives

Cyberbullying Detection on Social Media