The Neurocognitive Process of Digital Radicalization: A Theoretical Model and Analytical Framework

Recent studies suggest that empathy induced by narrative messages can effectively facilitate persuasion and reduce psychological reactance. Although limited, emerging research on the etiology of radical political behavior has begun to explore the role of narratives in shaping an individual’s beliefs, attitudes, and intentions that culminate in radicalization. The existing studies focus exclusively on the influence of narrative persuasion on an individual, but they overlook the necessity of empathy and that in the absence of empathy, persuasion is not salient. We argue that terrorist organizations are strategic in cultivating empathetic-persuasive messages using audiovisual materials, and disseminating their message within the digital medium. Therefore, in this paper we propose a theoretical model and analytical framework capable of helping us better understand the neurocognitive process of digital radicalization

Fake Content Detection in the Information Exponential Spreading Era

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementRecent years brought an information access democratization, allowing people to access a huge amount of information and the ability to share it, in a way that it can easily reach millions of people in a very short time. This allows to have right and wrong uses of this capabilities, that in some cases can be used to spread malicious content to achieve some sort of goal. Several studies have been made regarding text mining and sentiment analysis, aiming to spot fake information and avoid misinformation spreading. The trustworthiness and veracity of the information that is accessible to people is getting increasingly important, and in some cases critical, and can be seen has a huge challenge for the current digital era. This problem might be addressed with the help of science and technology. One question that we can do to ourselves is: How do we guarantee that there is a correct use of information, and that people can trust in the veracity of it? Using mathematics and statistics, combined with machine learning classification and predictive algorithms, using the current computation power of information systems, can help minimize the problem, or at least spot the potential fake information. One suggests developing a research work that aims to reach a model for the prediction of a given text content is trustworthy. The results were promising reaching a predicting model with good performance

Semantic and Sentiment Dissonant Framing Effects on Online News Sharing

Information artifacts incorporate cognitive elements in their design to inform users about and entice them to consume relevant content. Sparse research has examined how to design cognitive elements in information artifacts in the digital news platforms context. This study investigates how information artifacts’ semantic and sentiment elements convey meaning and emotion to elicit users to share online news. We propose a dissonant framework and hypothesize that three dissonance dimensions (namely, semantic dissonance, textual sentiment dissonance, and visual sentiment dissonance) influence news sharing. We tested the hypotheses using real-world data from 2013 to 2015 from Mashable—a popular digital news platform. We used novel machine-learning techniques to extract topics and sentiments from text and photos in news articles. Findings from our econometric analysis support that textual sentiment and visual sentiment dissonance positively affect news sharing

SENTIMENT(ALITY) AS A MEANS OF PERSUASION: ON THE THIN ICE OF MANIPULATION IN PROTESTANT SERMONS

Religious discourse represents an area of human communication in which persuasion plays a vital role; religious texts seem to be essentially related to the ultimate objective of religion: to create, mediate and legitimise ideology in order to persuade the reader of the veracity of the religious doctrine (Fairclough 1989, Cotterell & Turner 1989: 26-33, van Dijk 1998: 317). The paper seeks to investigate the persuasive strategies and linguistic means employed to convey persuasion in English Protestant sermons. The analysis focuses on the rhetorical role of pathos, which is purposefully evoked by the preacher via wilful employment of aff ect and emotions. Attention will also be paid to the blurred borderline between the intentional use of sentiment and sentimentality, and manipulation

On the Promotion of the Social Web Intelligence

Given the ever-growing information generated through various online social outlets, analytical research on social media has intensified in the past few years from all walks of life. In particular, works on social Web intelligence foster and benefit from the wisdom of the crowds and attempt to derive actionable information from such data. In the form of collective intelligence, crowds gather together and contribute to solving problems that may be difficult or impossible to solve by individuals and single computers. In addition, the consumer insight revealed from social footprints can be leveraged to build powerful business intelligence tools, enabling efficient and effective decision-making processes. This dissertation is broadly concerned with the intelligence that can emerge from the social Web platforms. In particular, the two phenomena of social privacy and online persuasion are identified as the two pillars of the social Web intelligence, studying which is essential in the promotion and advancement of both collective and business intelligence. The first part of the dissertation is focused on the phenomenon of social privacy. This work is mainly motivated by the privacy dichotomy problem. Users often face difficulties specifying privacy policies that are consistent with their actual privacy concerns and attitudes. As such, before making use of social data, it is imperative to employ multiple safeguards beyond the current privacy settings of users. As a possible solution, we utilize user social footprints to detect their privacy preferences automatically. An unsupervised collaborative filtering approach is proposed to characterize the attributes of publicly available accounts that are intended to be private. Unlike the majority of earlier studies, a variety of social data types is taken into account, including the social context, the published content, as well as the profile attributes of users. Our approach can provide support in making an informed decision whether to exploit one\u27s publicly available data to draw intelligence. With the aim of gaining insight into the strategies behind online persuasion, the second part of the dissertation studies written comments in online deliberations. Specifically, we explore different dimensions of the language, the temporal aspects of the communication, as well as the attributes of the participating users to understand what makes people change their beliefs. In addition, we investigate the factors that are perceived to be the reasons behind persuasion by the users. We link our findings to traditional persuasion research, hoping to uncover when and how they apply to online persuasion. A set of rhetorical relations is known to be of importance in persuasive discourse. We further study the automatic identification and disambiguation of such rhetorical relations, aiming to take a step closer towards automatic analysis of online persuasion. Finally, a small proof of concept tool is presented, showing the value of our persuasion and rhetoric studies

How Online Diaries Persuade Customers — The Role of Narratives

Online diary is a series of reviews in chronological order generated by customers to record their experience over time, which is a new type of online review emerging in the medical beauty industry. This study extends narrative transportation theory to explore the effect of the dynamic structure of online diaries on persuasion. We posit that emotional shift and utilitarian value can positively enhance online diary persuasion through improving transportation, and the relationship between the temporal flow and persuasion is converse U shape. The moderating role of social influence and visual content richness to the main effect is also investigated in this study. We collected real data to test our hypotheses utilizing Natural Language Processing (NLP) method and econometric model. This study is expected to make both theoretical and practical contributions

Sentiment, richness, authority, and relevance model of information sharing during social Crises—the case of #MH370 tweets

The study introduces a model of crisis information sharing based on Twitter discussions of the missing Malaysian Airlines Flight 370. Grounded in the Elaboration Likelihood Model, the study tests four salient factors: Sentiment, Richness, Authority, and Relevance, which can be measured by peripheral cues in tweets and in user profiles. Findings suggest that information sharing is positively associated with the presence of peripheral cues indicative of a confident, self-revealing and positive emotional language style, and is negatively related to an angry and informal style. Additionally, information sharing is related to the presence of multimedia cues and cues indicating source popularity

EMOTIONS THAT INFLUENCE PURCHASE DECISIONS AND THEIR ELECTRONIC PROCESSING

Recent studies have shown that most of our purchasing choices and decisions are theresult of a careful analysis of the advantages and disadvantages and of affective and emotionalaspects. Psychological literature recognizes that the emotional conditions are always present andinfluence every stage of decision-making in purchasing process. Consumers establish with companybrands an overall emotional relationship and express, also with web technologies, reviews andsuggestions on product/service. In our department we have developed an original algorithm ofsentiment analysis to extract emotions from online customer opinions. With this algorithm we haveobtained good results to polarize this opinions in order to reach strategic marketing goals.emotions, emotional marketing, emotional brand, emotions measurement, sentiment analysis.

A Retrospective Analysis of the Fake News Challenge Stance Detection Task

The 2017 Fake News Challenge Stage 1 (FNC-1) shared task addressed a stance classification task as a crucial first step towards detecting fake news. To date, there is no in-depth analysis paper to critically discuss FNC-1's experimental setup, reproduce the results, and draw conclusions for next-generation stance classification methods. In this paper, we provide such an in-depth analysis for the three top-performing systems. We first find that FNC-1's proposed evaluation metric favors the majority class, which can be easily classified, and thus overestimates the true discriminative power of the methods. Therefore, we propose a new F1-based metric yielding a changed system ranking. Next, we compare the features and architectures used, which leads to a novel feature-rich stacked LSTM model that performs on par with the best systems, but is superior in predicting minority classes. To understand the methods' ability to generalize, we derive a new dataset and perform both in-domain and cross-domain experiments. Our qualitative and quantitative study helps interpreting the original FNC-1 scores and understand which features help improving performance and why. Our new dataset and all source code used during the reproduction study are publicly available for future research