Detecting and Monitoring Hate Speech in Twitter

Social Media are sensors in the real world that can be used to measure the pulse of societies. However, the massive and unfiltered feed of messages posted in social media is a phenomenon that nowadays raises social alarms, especially when these messages contain hate speech targeted to a specific individual or group. In this context, governments and non-governmental organizations (NGOs) are concerned about the possible negative impact that these messages can have on individuals or on the society. In this paper, we present HaterNet, an intelligent system currently being used by the Spanish National Office Against Hate Crimes of the Spanish State Secretariat for Security that identifies and monitors the evolution of hate speech in Twitter. The contributions of this research are many-fold: (1) It introduces the first intelligent system that monitors and visualizes, using social network analysis techniques, hate speech in Social Media. (2) It introduces a novel public dataset on hate speech in Spanish consisting of 6000 expert-labeled tweets. (3) It compares several classification approaches based on different document representation strategies and text classification models. (4) The best approach consists of a combination of a LTSM+MLP neural network that takes as input the tweet’s word, emoji, and expression tokens’ embeddings enriched by the tf-idf, and obtains an area under the curve (AUC) of 0.828 on our dataset, outperforming previous methods presented in the literatureThe work by Quijano-Sanchez was supported by the Spanish Ministry of Science and Innovation grant FJCI-2016-28855. The research of Liberatore was supported by the Government of Spain, grant MTM2015-65803-R, and by the European Union’s Horizon 2020 Research and Innovation Programme, under the Marie Sklodowska-Curie grant agreement No. 691161 (GEOSAFE). All the financial support is gratefully acknowledge

Searching for associations between social media trending topics and organizations

This work focuses on how micro and small companies can take advantage of trending topics for marketing campaigns. Trending topics are the most discussed topics at the moment on social media platforms, particularly on Twitter and Facebook. While the access to trending topics is free and available to everyone, marketing specialists and specific software are more expensive, therefore small companies do not have the budget to support those costs. The main goal is to search for associations between trending topics and companies on social media platforms and HotRivers prototype is designed to accomplish this. A solution that aims to be inexpensive, fast, and automated. Detailed analyses were conducted to reduced the time and maximize the resources available at the lowest price. The final user receives a list of the trending topics related to the target company. For HotRivers were tested different pre-processing text techniques, a method to select tweets called Centroid Strategy and three models, an embedding vectors approach with Doc2Vec model, a probabilistic model with Latent Dirichlet Allocation, and a classification task approach with a Convolutional Neural Network used on the final architecture. The Centroid Strategy is used on trending topics to avoid unwanted tweets. In the results stand out that trending topic Nike has an association with the company Nike and #World- PatientSafetyDay has an association with Portsmouth Hospitals University. HotRivers cannot produce a full marketing campaign but can point out to the direction to the next campaign.Este trabalho foca-se na forma como as micro e pequenas empresas podem tirar partido dos trending topics para as suas campanhas de marketing. Os trending topics são os tópicos mais discutidos em cada momento nas redes sociais, particularmente no Twitter e no Facebook. Enquanto o acesso aos trending topics é gratuito e generalizado, os especialistas em marketing e o software especifico são dispendiosos, pelo que as pequenas empresas não têm o orçamento para suportar esses custos. O principal objetivo é procurar associações entre trending topics e empresas nas redes sociais e para isso foi criado um protótipo chamado HotRivers. Uma solução que pretende ser acessível, rápida e automatizada. Foram realizadas análises detalhadas para reduzir o tempo e maximizar os recursos disponíveis a preço baixo. O utilizador final recebe uma lista dos trending topics relacionados com a empresa alvo. O HotRivers foi testado com diferentes técnicas de pré-processamento de texto, um método para selecionar tweets chamado Estratégia Centroid e três modelos, uma abordagem de vectores embedding com o modelo Doc2Vec, um modelo probabilístico com Alocação de Dirichlet Latente, e uma abordagem de classificação com uma Rede Neural Convolucional, selecionada para a arquitetura final. A Estratégia Centroid é utilizada nos trending topics para evitar tweets indesejados. Nos resultados destacam-se o trending topic "Nike" que tem uma associação com a empresa Nike e #WorldPatientSafetyDay que tem uma associação com a Universidade dos Hospitais de Portsmouth. Embora o HotRivers não possa produzir uma campanha de marketing completa, pode apontar a direção para a campanha seguinte

Comparison of explicit vs. implicit measurements in predicting food purchases

In this study, we aimed to investigate the relation between consumer purchases of three branded blueberry flavored quarks and respective responses of the same consumers to these products using 1) traditional explicit consumer surveys measuring verbalized impressions, 2) novel explicit pictorial emoji scores and 3) implicit behavioral responses produced during an approach-avoidance task (AAT). Explicit measures (n=134) were collected before product tasting (expectation condition) during an online survey, and after product tasting (perception condition) during a Central Location Test (CLT). Implicit measures were collected with a subset of 56 randomly selected subjects during the CLT. These included electroencephalographic (EEG) measures, joystick response speed and pupil size responses. During one month following the CLT, respondents registered their purchases via an online diary. Bivariate correlations indicated that explicit scores correlate better with product purchase amounts in the perception condition than in the expectation condition. Furthermore, verbalized ratings correlated better with product purchase amounts than pictorial emoji scores. Of the implicit responses, EEG responses produced the strongest correlations with purchase behavior, similar to those observed for verbalized explicit ratings in the expectation condition. Multiple linear regression modelling indicated that the best-fitting model consisted of an emoji score, purchase intention score, pleasantness score, brand relationship score, and implicit joystick response speed. Overall, purchase behavior was associated stronger with explicit responses than with implicit responses. Yet, the prominent role of implicit joystick response speed in the multivariate regression model suggests its unique contribution to the understanding of purchase behavior.Peer reviewe

Social Media Fashion Knowledge Extraction as Captioning

Social media plays a significant role in boosting the fashion industry, where a massive amount of fashion-related posts are generated every day. In order to obtain the rich fashion information from the posts, we study the task of social media fashion knowledge extraction. Fashion knowledge, which typically consists of the occasion, person attributes, and fashion item information, can be effectively represented as a set of tuples. Most previous studies on fashion knowledge extraction are based on the fashion product images without considering the rich text information in social media posts. Existing work on fashion knowledge extraction in social media is classification-based and requires to manually determine a set of fashion knowledge categories in advance. In our work, we propose to cast the task as a captioning problem to capture the interplay of the multimodal post information. Specifically, we transform the fashion knowledge tuples into a natural language caption with a sentence transformation method. Our framework then aims to generate the sentence-based fashion knowledge directly from the social media post. Inspired by the big success of pre-trained models, we build our model based on a multimodal pre-trained generative model and design several auxiliary tasks for enhancing the knowledge extraction. Since there is no existing dataset which can be directly borrowed to our task, we introduce a dataset consisting of social media posts with manual fashion knowledge annotation. Extensive experiments are conducted to demonstrate the effectiveness of our model.Comment: Accepted by SIGIR-AP 202

Technology and Organizational Decision-Making: A Qualitative Case Study Approach

Technology and communication skills simultaneously increase organizational productivity and decision-making. However, excessive use of technology to make decisions can diminish the added benefits that nonverbal communication can bring. The interpersonal sensitivity effects on perception of service quality model, face-to-face communication versus teleconferencing, the technology acceptance model, and decision-making were the conceptual framework of the study. The research questions examined how excessive use of technology to make decisions can diminish the added benefits that nonverbal communication can bring to organizational leadership decision-making. Employing an exploratory multiple case study design, organizational leaders from the Southern California region, ranging in seniority from team-leader through executive, completed 25 member-checked interviews and 15 qualitative questionnaires. Using Yin\u27s 5-step approach to analyzing the data, 8 themes remerged. From these themes, I developed 5 findings regarding technology, non-verbal communication, and decision-making. My study affects positive social change by educating organizational leaders on the importance of distancing themselves from work during nonwork hours, encouraging organizational leaders to develop guidelines around the use of teleconference software, promoting organizational learning with an emphasis on soft-skill training, and acknowledging when there is a misunderstanding in nonverbal communication. Organizational leaders can improve decision-making by using the favorable traits associated with both electronic and nonverbal behavioral communications

Novel Techniques to Measure the Sensory, Emotional, and Physiological (Biometric) Responses of Consumers toward Foods and Packaging

This book reprinted from articles published in the Special Issue “Novel Techniques to Measure the Sensory, Emotional, and Physiological (Biometric) Responses of Consumers toward Foods and Packaging” of the journal Foods aims to provide a deeper understanding of novel techniques to measure the different sensory, emotional, and physiological responses toward foods. The editor hopes that the findings from this Special Issue can help the broader scientific community to understand the use of novel sensory science techniques that can be used in the evaluation of products

TBCOV: Two Billion Multilingual COVID-19 Tweets with Sentiment, Entity, Geo, and Gender Labels

The widespread usage of social networks during mass convergence events, such as health emergencies and disease outbreaks, provides instant access to citizen-generated data that carry rich information about public opinions, sentiments, urgent needs, and situational reports. Such information can help authorities understand the emergent situation and react accordingly. Moreover, social media plays a vital role in tackling misinformation and disinformation. This work presents TBCOV, a large-scale Twitter dataset comprising more than two billion multilingual tweets related to the COVID-19 pandemic collected worldwide over a continuous period of more than one year. More importantly, several state-of-the-art deep learning models are used to enrich the data with important attributes, including sentiment labels, named-entities (e.g., mentions of persons, organizations, locations), user types, and gender information. Last but not least, a geotagging method is proposed to assign country, state, county, and city information to tweets, enabling a myriad of data analysis tasks to understand real-world issues. Our sentiment and trend analyses reveal interesting insights and confirm TBCOV's broad coverage of important topics.Comment: 20 pages, 13 figures, 8 table

A Twitter narrative of the COVID-19 pandemic in Australia

Social media platforms contain abundant data that can provide comprehensive knowledge of historical and real-time events. During crisis events, the use of social media peaks, as people discuss what they have seen, heard, or felt. Previous studies confirm the usefulness of such socially generated discussions for the public, first responders, and decision-makers to gain a better understanding of events as they unfold at the ground level. This study performs an extensive analysis of COVID-19-related Twitter discussions generated in Australia between January 2020, and October 2022. We explore the Australian Twitterverse by employing state-of-the-art approaches from both supervised and unsupervised domains to perform network analysis, topic modeling, sentiment analysis, and causality analysis. As the presented results provide a comprehensive understanding of the Australian Twitterverse during the COVID-19 pandemic, this study aims to explore the discussion dynamics to aid the development of future automated information systems for epidemic/pandemic management.Comment: Accepted to ISCRAM 202