2,348 research outputs found
Detecting Deception, Partisan, and Social Biases
Tesis por compendio[ES] En la actualidad, el mundo polÃtico tiene tanto o más impacto en la sociedad que ésta en el mundo polÃtico. Los lÃderes o representantes de partidos polÃticos hacen uso de su poder en los medios de comunicación, para modificar posiciones ideológicas y llegar al pueblo con el objetivo de ganar popularidad en las elecciones gubernamentales.A través de un lenguaje engañoso, los textos polÃticos pueden contener sesgos partidistas y sociales que minan la percepción de la realidad. Como resultado, los seguidores de una ideologÃa, o miembros de una categorÃa social, se sienten amenazados por otros grupos sociales o ideológicos, o los perciben como competencia, derivándose asà una polarización polÃtica con agresiones fÃsicas y verbales.
La comunidad cientÃfica del Procesamiento del Lenguaje Natural (NLP, según sus siglas en inglés) contribuye cada dÃa a detectar discursos de odio, insultos, mensajes ofensivos, e información falsa entre otras tareas computacionales que colindan con ciencias sociales. Sin embargo, para abordar tales tareas, es necesario hacer frente a diversos problemas entre los que se encuentran la dificultad de tener textos etiquetados, las limitaciones de no trabajar con un equipo interdisciplinario, y los desafÃos que entraña la necesidad de soluciones interpretables por el ser humano.
Esta tesis se enfoca en la detección de sesgos partidistas y sesgos sociales, tomando como casos de estudio el hiperpartidismo y los estereotipos sobre inmigrantes. Para ello, se propone un modelo basado en una técnica de enmascaramiento de textos capaz de detectar lenguaje engañoso incluso en temas controversiales, siendo capaz de capturar patrones del contenido y el estilo de escritura. Además, abordamos el problema usando modelos basados en BERT, conocidos por su efectividad al capturar patrones sintácticos y semánticos sobre las mismas representaciones de textos. Ambos enfoques, la técnica de enmascaramiento y los modelos basados en BERT, se comparan en términos de desempeño y explicabilidad en la detección de hiperpartidismo en noticias polÃticas y estereotipos sobre inmigrantes. Para la identificación de estos últimos, se propone una nueva taxonomÃa con fundamentos teóricos en sicologÃa social, y con la que se etiquetan textos extraÃdos de intervenciones partidistas llevadas a cabo en el Parlamento español. Los resultados muestran que los enfoques propuestos contribuyen al estudio del hiperpartidismo, asà como a identif i car cuándo los ciudadanos y polÃticos enmarcan a los inmigrantes en una imagen de vÃctima, recurso económico, o amenaza. Finalmente, en esta investigación interdisciplinaria se demuestra que los estereotipos sobre inmigrantes son usados como estrategia retórica en contextos polÃticos.[CA] Avui, el món polÃtic té tant o més impacte en la societat que la societat en el món polÃtic. Els lÃders polÃtics, o representants dels partits polÃtics, fan servir el seu poder als mitjans de comunicació per modif i car posicions ideològiques i arribar al poble per tal de guanyar popularitat a les eleccions governamentals. Mitjançant un llenguatge enganyós, els textos polÃtics poden contenir biaixos partidistes i socials que soscaven la percepció de la realitat. Com a resultat, augmenta la polarització polÃtica nociva perquè els seguidors d'una ideologia, o els membres d'una categoria social, veuen els altres grups com una amenaça o competència, que acaba en agressions verbals i fÃsiques amb resultats desafortunats.
La comunitat de Processament del llenguatge natural (PNL) té cada dia noves aportacions amb enfocaments que ajuden a detectar discursos d'odi, insults, missatges ofensius i informació falsa, entre altres tasques computacionals relacionades amb les ciències socials. No obstant això, molts obstacles impedeixen eradicar aquests problemes, com ara la dif i cultat de tenir textos anotats, les limitacions dels enfocaments no interdisciplinaris i el repte afegit per la necessitat de solucions interpretables.
Aquesta tesi se centra en la detecció de biaixos partidistes i socials, prenent com a cas prà ctic l'hiperpartidisme i els estereotips sobre els immigrants.
Proposem un model basat en una tècnica d'emmascarament que permet detectar llenguatge enganyós en temes polèmics i no polèmics, capturant pa-trons relacionats amb l'estil i el contingut. A més, abordem el problema avaluant models basats en BERT, coneguts per ser efectius per capturar patrons semà ntics i sintà ctics en la mateixa representació. Comparem aquests dos enfocaments (la tècnica d'emmascarament i els models basats en BERT) en termes de rendiment i les seves solucions explicables en la detecció de l'hiperpartidisme en les notÃcies polÃtiques i els estereotips d'immigrants.
Per tal d'identificar els estereotips dels immigrants, proposem una nova tax-onomia recolzada per la teoria de la psicologia social i anotem un conjunt de dades de les intervencions partidistes al Parlament espanyol. Els resultats mostren que els nostres models poden ajudar a estudiar l'hiperpartidisme i identif i car diferents marcs en què els ciutadans i els polÃtics perceben els immigrants com a vÃctimes, recursos econòmics o amenaces. Finalment, aquesta investigació interdisciplinà ria demostra que els estereotips dels immigrants s'utilitzen com a estratègia retòrica en contextos polÃtics.[EN] Today, the political world has as much or more impact on society than society has on the political world. Political leaders, or representatives of political parties, use their power in the media to modify ideological positions and reach the people in order to gain popularity in government elections.
Through deceptive language, political texts may contain partisan and social biases that undermine the perception of reality. As a result, harmful political polarization increases because the followers of an ideology, or members of a social category, see other groups as a threat or competition, ending in verbal and physical aggression with unfortunate outcomes.
The Natural Language Processing (NLP) community has new contri-butions every day with approaches that help detect hate speech, insults, of f ensive messages, and false information, among other computational tasks related to social sciences. However, many obstacles prevent eradicating these problems, such as the dif f i culty of having annotated texts, the limitations of non-interdisciplinary approaches, and the challenge added by the necessity of interpretable solutions.
This thesis focuses on the detection of partisan and social biases, tak-ing hyperpartisanship and stereotypes about immigrants as case studies. We propose a model based on a masking technique that can detect deceptive language in controversial and non-controversial topics, capturing patterns related to style and content. Moreover, we address the problem by evalu-ating BERT-based models, known to be ef f ective at capturing semantic and syntactic patterns in the same representation. We compare these two approaches (the masking technique and the BERT-based models) in terms of their performance and the explainability of their decisions in the detection of hyperpartisanship in political news and immigrant stereotypes. In order to identify immigrant stereotypes, we propose a new taxonomy supported by social psychology theory and annotate a dataset from partisan interventions in the Spanish parliament. Results show that our models can help study hyperpartisanship and identify dif f erent frames in which citizens and politicians perceive immigrants as victims, economic resources, or threat. Finally, this interdisciplinary research proves that immigrant stereotypes are used as a rhetorical strategy in political contexts.This PhD thesis was funded by the MISMIS-FAKEnHATE research project
(PGC2018-096212-B-C31) of the Spanish Ministry of Science and Innovation.Sánchez Junquera, JJ. (2022). Detecting Deception, Partisan, and Social Biases [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/185784Compendi
Predicting the Law Area and Decisions of French Supreme Court Cases
In this paper, we investigate the application of text classification methods
to predict the law area and the decision of cases judged by the French Supreme
Court. We also investigate the influence of the time period in which a ruling
was made over the textual form of the case description and the extent to which
it is necessary to mask the judge's motivation for a ruling to emulate a
real-world test scenario. We report results of 96% f1 score in predicting a
case ruling, 90% f1 score in predicting the law area of a case, and 75.9% f1
score in estimating the time span when a ruling has been issued using a linear
Support Vector Machine (SVM) classifier trained on lexical features.Comment: RANLP 201
Deception Detection with Feature-Augmentation by soft Domain Transfer
In this era of information explosion, deceivers use different domains or
mediums of information to exploit the users, such as News, Emails, and Tweets.
Although numerous research has been done to detect deception in all these
domains, information shortage in a new event necessitates these domains to
associate with each other to battle deception. To form this association, we
propose a feature augmentation method by harnessing the intermediate layer
representation of neural models. Our approaches provide an improvement over the
self-domain baseline models by up to 6.60%. We find Tweets to be the most
helpful information provider for Fake News and Phishing Email detection,
whereas News helps most in Tweet Rumor detection. Our analysis provides a
useful insight for domain knowledge transfer which can help build a stronger
deception detection system than the existing literature
The Effectiveness of Deceptive Tactics in Phishing
Phishing, or the attempt of criminals to obtain sensitive information through a variety of techniques, is still a serious problem for IT managers and Internet consumers. With over 57 million Americans exposed to phishing in 2005, a reported 5% of recipients were victimized. Some believe that one percent of all email is phishing-related, and estimates of financial losses vary from 100 million to 1 billion dollars (US) a year (Goth, 2005). Our research examines the properties in a phishing email that may or may not influence the users to give out personal and sensitive information. For this field experiment we use students to test the effect that certain types of content have on the phishing process. The study outcomes suggest that user’s do not pay attention to the sender’s domain in a phishing email but do respond to personalized messages and messages that demand an immediate response
Deception Tactics and Counterfeit Deception in Online Environments
With widespread globalization happening at an alarming speed, the manufacturing and copying of goods has become a matter of routine for counterfeiters. The Internet has provided a new advantage for counterfeiters - the opportunity to sell goods without prior consumer inspection. Leveraging this opportunity, deceitful purveyors of imitation goods engage in unethical practices such as selling counterfeit goods presenting them as genuine. We propose that there are two categories of counterfeit deception mechanisms online: product level information and seller level information. In order to successfully deceive prospective buyers, sellers conceal the signals that identify the offering as a fake, and present themselves as legitimate business entities. In this research-in-progress paper, we outline several propositions to guide future research in this area. We are currently conducting an empirical study to test these propositions
Perverse effects of other-referenced performance goals in an information exchange context
A values-centered leadership model comprised of leader stakeholder and economic values, follower values congruence, and responsible leadership outcomes was tested using data from 122 organizational leaders and 458 of their direct reports. Alleviating same-source bias concerns in leadership survey research, follower ratings of leadership style and follower ratings of values congruence and responsible leadership outcomes were collected from separate sources via the split-sample methodology. Results of structural equation modeling analyses demonstrated that leader stakeholder values predicted transformational leadership, whereas leader economic values were associated with transactional leadership. Follower values congruence was strongly associated with transformational leadership, unrelated to transactional leadership, and partially mediated the relationships between transformational leadership and both follower organizational citizenship behaviors and follower beliefs in the stakeholder view of corporate social responsibility. Implications for responsible leadership and transformational leadership theory, practice, and future research are discussed
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