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.

/pdf/political-discourse-content-analysis-a-critical-overview-of-3ij9zach6y.pdf

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

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.

/pdf/challenges-and-frontiers-in-abusive-content-detection-smv3qstyti.pdf

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

Aspect-based sentiment analysis (ABSA) involves three subtasks, i.e., aspect term extraction, opinion term extraction, and aspect-level sentiment classification. Most existing studies focused on one of these subtasks only. Several recent researches made successful attempts to solve the complete ABSA problem with a unified framework. However, the interactive relations among three subtasks are still under-exploited. We argue that such relations encode collaborative signals between different subtasks. For example, when the opinion term is ``delicious'', the aspect term must be ``food'' rather than ``place''. In order to fully exploit these relations, we propose a Relation-Aware Collaborative Learning (RACL) framework which allows the subtasks to work coordinately via the multi-task learning and relation propagation mechanisms in a stacked multi-layer network. Extensive experiments on three real-world datasets demonstrate that RACL significantly outperforms the state-of-the-art methods for the complete ABSA task.

/pdf/relation-aware-collaborative-learning-for-unified-aspect-33nzo2wjni.pdf

Relation-Aware Collaborative Learning for Unified Aspect-Based Sentiment Analysis

While social media offer great communication opportunities, they also increase the vulnerability of young people to threatening situations online. Recent studies report that cyberbullying constitutes a growing problem among youngsters. Successful prevention depends on the adequate detection of potentially harmful messages and the information overload on the Web requires intelligent systems to identify potential risks automatically. The focus of this paper is on automatic cyberbullying detection in social media text by modelling posts written by bullies, victims, and bystanders of online bullying. We describe the collection and fine-grained annotation of a cyberbullying corpus for English and Dutch and perform a series of binary classification experiments to determine the feasibility of automatic cyberbullying detection. We make use of linear support vector machines exploiting a rich feature set and investigate which information sources contribute the most for the task. Experiments on a hold-out test set reveal promising results for the detection of cyberbullying-related posts. After optimisation of the hyperparameters, the classifier yields an F1 score of 64% and 61% for English and Dutch respectively, and considerably outperforms baseline systems.

/pdf/automatic-detection-of-cyberbullying-in-social-media-text-2l5z18yzoc.pdf

Automatic detection of cyberbullying in social media text

This paper presents a dataset and supervised classification approach for economic event detection in English news articles. Currently, the economic domain is lacking resources and methods for data-driven supervised event detection. The detection task is conceived as a sentence-level classification task for 10 different economic event types. Two different machine learning approaches were tested: a rich feature set Support Vector Machine (SVM) set-up and a word-vector-based long short-term memory recurrent neural network (RNN-LSTM) set-up. We show satisfactory results for most event types, with the linear kernel SVM outperforming the other experimental set-ups.

https://biblio.ugent.be/publication/8570479/file/8573942.pdf

Economic Event Detection in Company-Specific News Text

The detection of online cyberbullying has seen an increase in societal importance, popularity in research, and available open data. Nevertheless, while computational power and affordability of resources continue to increase, the access restrictions on high-quality data limit the applicability of state-of-the-art techniques. Consequently, much of the recent research uses small, heterogeneous datasets, without a thorough evaluation of applicability. In this paper, we further illustrate these issues, as we (i) evaluate many publicly available resources for this task and demonstrate difficulties with data collection. These predominantly yield small datasets that fail to capture the required complex social dynamics and impede direct comparison of progress. We (ii) conduct an extensive set of experiments that indicate a general lack of cross-domain generalization of classifiers trained on these sources, and openly provide this framework to replicate and extend our evaluation criteria. Finally, we (iii) present an effective crowdsourcing method: simulating real-life bullying scenarios in a lab setting generates plausible data that can be effectively used to enrich real data. This largely circumvents the restrictions on data that can be collected, and increases classifier performance. We believe these contributions can aid in improving the empirical practices of future research in the field.

/pdf/current-limitations-in-cyberbullying-detection-on-evaluation-2qmz18xny5.pdf

Current Limitations in Cyberbullying Detection: on Evaluation Criteria, Reproducibility, and Data Scarcity

This paper presents an integrated ABSA pipeline for Dutch that has been developed and tested on qualitative user feedback coming from three domains: retail, bank- ing and human resources. The two lat- ter domains provide service-oriented data, which has not been investigated before in ABSA. By performing in-domain and cross-domain experiments the validity of our approach was investigated. We show promising results for the three ABSA sub- tasks, aspect term extraction, aspect cat- egory classification and aspect polarity classification.

/pdf/towards-an-integrated-pipeline-for-aspect-based-sentiment-21u6dml3la.pdf

Towards an integrated pipeline for aspect-based sentiment analysis in various domains

Successful prevention of cyberbullying depends on the adequate detection of harmful messages. Given the impossibility of human moderation on the Social Web, intelligent systems are required to identify clues of cyberbullying automatically. Much work on cyberbullying detection focuses on detecting abusive language without analyzing the severity of the event nor the participants involved. Automatic analysis of participant roles in cyberbullying traces enables targeted bullying prevention strategies. In this paper, we aim to automatically detect different participant roles involved in textual cyberbullying traces, including bullies, victims, and bystanders. We describe the construction of two cyberbullying corpora (a Dutch and English corpus), that were both manually annotated with bullying types and participant roles and we perform a series of multiclass classification experiments to determine the feasibility of text-based cyberbullying participant role detection. The representative datasets present a data imbalance problem for which we investigate feature filtering and data resampling as skew mitigation techniques. We investigate the performance of feature-engineered single and ensemble classifier setups as well as transformer-based pre-trained language models. Cross-validation experiments revealed promising results for the detection of cyberbullying roles using pre-trained language model fine-tuning techniques, with the best classifier for English (RoBERTa) yielding a macro-averaged F1-score of 55.84%, and the best one for Dutch (RobBERT) yielding an F1-score of 56.73%. Experiment replication data and source code is available at https://osf.io/nb2r3.

https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S135132492000056X

Gilles Jacobs

Papers

Automatic detection of cyberbullying in social media text

Economic Event Detection in Company-Specific News Text

Current Limitations in Cyberbullying Detection: on Evaluation Criteria, Reproducibility, and Data Scarcity

Towards an integrated pipeline for aspect-based sentiment analysis in various domains

Automatic classification of participant roles in cyberbullying: Can we detect victims, bullies, and bystanders in social media text?