Analysis of Twitter data for postmarketing surveillance in pharmacovigilance

Postmarketing surveillance (PMS) has the vital aim to monitor effects of drugs af- ter release for use by the general pop- ulation, but suffers from under-reporting and limited coverage. Automatic meth- ods for detecting drug effect reports, es- pecially for social media, could vastly in- crease the scope of PMS. Very few auto- matic PMS methods are currently avail- able, in particular for the messy text types encountered on Twitter. In this paper we describe first results for developing PMS methods specifically for tweets. We de- scribe the corpus of 125,669 tweets we have created and annotated to train and test the tools. We find that generic tools per- form well for tweet-level language iden- tification and tweet-level sentiment anal- ysis (both 0.94 F1-Score). For detection of effect mentions we are able to achieve 0.87 F1-Score, while effect-level adverse- vs.-beneficial analysis proves harder with an F1-Score of 0.64. Among other things, our results indicate that MetaMap seman- tic types provide a very promising ba- sis for identifying drug effect mentions in tweets

Sentiment Analysis for Fake News Detection

[Abstract] In recent years, we have witnessed a rise in fake news, i.e., provably false pieces of information created with the intention of deception. The dissemination of this type of news poses a serious threat to cohesion and social well-being, since it fosters political polarization and the distrust of people with respect to their leaders. The huge amount of news that is disseminated through social media makes manual verification unfeasible, which has promoted the design and implementation of automatic systems for fake news detection. The creators of fake news use various stylistic tricks to promote the success of their creations, with one of them being to excite the sentiments of the recipients. This has led to sentiment analysis, the part of text analytics in charge of determining the polarity and strength of sentiments expressed in a text, to be used in fake news detection approaches, either as a basis of the system or as a complementary element. In this article, we study the different uses of sentiment analysis in the detection of fake news, with a discussion of the most relevant elements and shortcomings, and the requirements that should be met in the near future, such as multilingualism, explainability, mitigation of biases, or treatment of multimedia elements.Xunta de Galicia; ED431G 2019/01Xunta de Galicia; ED431C 2020/11This work has been funded by FEDER/Ministerio de Ciencia, Innovación y Universidades — Agencia Estatal de Investigación through the ANSWERASAP project (TIN2017-85160-C2-1-R); and by Xunta de Galicia through a Competitive Reference Group grant (ED431C 2020/11). CITIC, as Research Center of the Galician University System, is funded by the Consellería de Educación, Universidade e Formación Profesional of the Xunta de Galicia through the European Regional Development Fund (ERDF/FEDER) with 80%, the Galicia ERDF 2014-20 Operational Programme, and the remaining 20% from the Secretaría Xeral de Universidades (ref. ED431G 2019/01). David Vilares is also supported by a 2020 Leonardo Grant for Researchers and Cultural Creators from the BBVA Foundation. Carlos Gómez-Rodríguez has also received funding from the European Research Council (ERC), under the European Union’s Horizon 2020 research and innovation programme (FASTPARSE, grant No. 714150

Lying to identity: analysis of latencies from interviews.

openDetecting liars of personal identities is becoming an increasingly important goal. However, an obstacle to this endeavor is that deceivers can prepare a "lie script" prior to investigative interviews, producing narratives that are indistinguishable from those of truth tellers. To overcome this limitation, specific interview techniques have been developed that pose cognitive disadvantages for deceivers, such as including unexpected questions alongside control and expected questions. Unexpected questions can be considered a "rehearsal averting strategy" since liars cannot anticipate and prepare responses in advance. Consequently, when confronted with unexpected questions, liars are compelled to generate an immediate deceptive statement, inhibit the truth, and replace it with a fabricated narrative, while ensuring that the deception remains undetectable to the interviewer. This process of information reconstruction leads to increased response times and error rates for unexpected questions. Even truth tellers will experience an increase in cognitive load when responding to unexpected questions, but their responses, based on genuine memory traces, will be more comparable. The purpose of this study is to assess whether it is possible to discriminate between identity liars and truth tellers by analyzing response times and errors obtained from face-to-face interviews that implement unexpected questions.Detecting liars of personal identities is becoming an increasingly important goal. However, an obstacle to this endeavor is that deceivers can prepare a "lie script" prior to investigative interviews, producing narratives that are indistinguishable from those of truth tellers. To overcome this limitation, specific interview techniques have been developed that pose cognitive disadvantages for deceivers, such as including unexpected questions alongside control and expected questions. Unexpected questions can be considered a "rehearsal averting strategy" since liars cannot anticipate and prepare responses in advance. Consequently, when confronted with unexpected questions, liars are compelled to generate an immediate deceptive statement, inhibit the truth, and replace it with a fabricated narrative, while ensuring that the deception remains undetectable to the interviewer. This process of information reconstruction leads to increased response times and error rates for unexpected questions. Even truth tellers will experience an increase in cognitive load when responding to unexpected questions, but their responses, based on genuine memory traces, will be more comparable. The purpose of this study is to assess whether it is possible to discriminate between identity liars and truth tellers by analyzing response times and errors obtained from face-to-face interviews that implement unexpected questions

Real-world, high-stakes deceptive speech: Theoretical validation and an examination of its potential for detection automation

The study of deception and the theories which have been developed have relied heavily on laboratory experiments, in controlled environments, utilizing American college students, participating in mock scenarios. The goal of this study was to validate previous deception research in a real-world high-stakes environment. An additional focus of this study was the development of procedures to process data (e.g. video or audio recordings) from real-world environments in such a manner that behavioral measures can be extracted and analyzed. This study utilized previously confirmed speech cues and constructs to deception in an attempt to validate a leading deception theory, Interpersonal Deception Theory (IDT). Several measures and constructs, utilized and validated in existing research, were explored and validated in this study. The data analyzed came from an adjudicated real-world high-stakes criminal case in which the subject was sentenced in federal court to 470 years in prison for creating child pornography, rape, sexual exploitation of children, child sexual assault and kidnapping; a crime spree that spanned over a five years and four states. The results did validate IDT with mixed results on individual measures and their constructs. The exploratory nature of the study, the volume of data, and the numerous methods of analysis used generated many possibilities for future research