How Emotions Unfold in Online Discussions After a Terror Attack

In the wake of a terror attack, social media is used for sharing thoughts and emotions, accessing and distributing information, and memorializing victims. Emotions are a big part of this, but there is a gap in our understanding on how those emotions evolve and what kinds of social media uses they are related to. Better understanding of the emotional and topical developments of online discussions can serve not only to fill the aforementioned gap, but also assist in developing better collective coping strategies for recovering from terror attacks. We examine what types of conversations unfolded online after the Boston Marathon Bombing and what kinds of emotions were associated with them, accounting for regional differences, and present a process model covering the general trends of such conversations. Although the phases apply to reactions to terror attacks on a general level, there are proximity-based differences to the location of the terror attack

Identifying Emotions in Social Media: Comparison of Word-emotion lexica

In recent years, emotions expressed in social media messages have become a vivid research topic due to their influence on the spread of misinformation and online radicalization over online social networks. Thus, it is important to correctly identify emotions in order to make inferences from social media messages. In this paper, we report on the performance of three publicly available word-emotion lexicons (NRC, DepecheMood, EmoSenticNet) over a set of Facebook and Twitter messages. To this end, we designed and implemented an algorithm that applies natural language processing (NLP) techniques along with a number of heuristics that reflect the way humans naturally assess emotions in written texts. In order to evaluate the appropriateness of the obtained emotion scores, we conducted a questionnaire-based survey with human raters. Our results show that there are noticeable differences between the performance of the lexicons as well as with respect to emotion scores the human raters provided in our surve

Leveraging Natural Language Processing to Analyse the Temporal Behavior of Extremists on Social Media

Aiming at achieving sustainability and quality of life for citizens, future smart cities adopt a data-centric approach to decision making in which assets, people, and events are constantly monitored to inform decisions. Public opinion monitoring is of particular importance to governments and intelligence agencies, who seek to monitor extreme views and attempts of radicalizing individuals in society. While social media platforms provide increased visibility and a platform to express public views freely, such platforms can also be used to manipulate public opinion, spread hate speech, and radicalize others. Natural language processing and data mining techniques have gained popularity for the analysis of social media content and the detection of extremists and radical views expressed online. However, existing approaches simplify the concept of radicalization to a binary problem in which individuals are classified as extremists or non-extremists. Such binary approaches do not capture the radicalization process\u27s complexity that is influenced by many aspects such as social interactions, the impact of opinion leaders, and peer pressure. Moreover, the longitudinal analysis of users\u27 interactions and profile evolution over time is lacking in the literature. Aiming at addressing those limitations, this work proposes a sophisticated framework for the analysis of the temporal behavior of extremists on social media platforms. Far-right extremism during the Trump presidency was used as a case study, and a large dataset of over 259,000 tweets was collected to train and test our models. The results obtained are very promising and encourage the use of advanced social media analytics in the support of effective and timely decision-making

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

#ParisAttack : Making sense of a terrorist attack in Twitter

Pariisissa 13. marraskuuta 2015 tapahtui seitsemän terrori-iskun sarja, jossa uhriluku nousi 129 henkeen ja loukkaantuneita oli noin 352. Terrori-isku sai paljon mediahuomiota osakseen ja sen takana oli terroristijärjestö ISIS (The Islamic State of Iraq and Syria). Keskustelu eri sosiaalisen median kanavissa oli vilkasta iskujen jälkeen. Tämä Pro gradu –tutkielma keskittyy terrori-iskun jälkeiseen keskusteluun ja ihmisten ensireaktioihin Twitterissä. Koska aikaisempaa tutkimusta tämän tyyppisen kriisin ensireaktioista on hyvin rajallisesti, data, jota tässä tutkielmassa käsitellään, rajoittuu tviitteihin, jotka lähetettiin neljän päivän sisällä iskuista. Tutkimuksen tavoitteena oli mallintaa millaisia ensireaktioita ihmisillä oli Islamin nimeen tehtyjen terroristi-iskujen jälkeen, mitkä teemat tviiteissä nousivat esiin, mihin tarkoitukseen Twitteriä käytettiin ja minkälainen rooli uskonnolla oli ihmisten järkeistämisprosessissa (sense-making). Tämän tutkielman tutkimusstrategiana on tapaustutkimus. Data kerättiin Twitteristä Pulsar nimisellä työkalulla. Datan rajaamiseksi käytettiin aihetunnisteita #parisattack, #parisshooting ja #paristerror sekä ajallista ja kieleen liittyvää rajaamista. Tiedon analysoinnin metodina käytettiin sisältöanalyysia. Tutkimuksen perusteella, Twitteriä käytettiin laajasti Pariisin terrori-iskujen jälkeen ja tiedon jakamisen tarve korostui Twitterin ensireaktioissa. Muita syitä tviittaamiseen olivat mielipiteiden jakaminen tai hallitsevan tunteen ilmaiseminen. Uskonto esiintyi suhteellisen pienessä osassa tviittejä. Nämä löydökset tukevat aikaisempaa tutkimusta tiedon saamisen tärkeydestä alkuvaiheessa kriisitilanteen tapahduttua, ja siten selittää pientä uskontoa käsittelevien tviittien osuutta. Kun dataa tarkasteltiin vain uskontoaiheisten tviittien osalta, mielipiteiden osuus korostui. Suuri osa näistä tviiteistä pyrki edistämään rauhanomaista yhteisymmärrystä (concensus) pääviesteinään se, että Muslimeja, Islamia tai uskontoa ei ole syyttäminen terrori-iskuista. Toisaalta noin neljännes tviiteistä piti edellä mainittuja syyllisenä iskuihin ja pyrkivät aiheuttamaan vastakkainasettelua (confrontation). Nämä löydökset viittaavat siihen, että uskonto jakoi mielipiteitä ja siitä etsittiin syitä terrori-iskuihin. Tämän tutkimuksen mukaan uskonto oli osa ihmisten järkeistämisprosessia uskontoaiheisten tviittien pienestä lukumäärästä huolimatta

From Mediatized Emotion to Digital Affect Cultures : New Technologies and Global Flows of Emotion

Research on the processes of mediatization aims to explore the mutual shaping of media and social life and how new media technologies influence and infiltrate social practices and cultural life. We extend this discussion of media’s role in transforming the everyday by including in the discussion the mediatization of emotion and discuss what we conceptualize as digital affect culture(s). We understand these as relational, contextual, globally emergent spaces in the digital environment where affective flows construct atmospheres of emotional and cultural belonging by way of emotional resonance and alignment. Approaching emotion as a cultural practice, in terms of affect, as something people do instead of have, we discuss how digital affect culture(s) traverse the digital terrains and construct pockets of culture-specific communities of affective practice. We draw on existing empirical research on digital memorial culture to empirically illustrate how digital affect culture manifests on micro, meso, and macro levels and elaborate on the constitutive characteristics of digital affect culture. We conclude with implications of this conceptualization for theoretical advancement and empirical research.Peer reviewe

Quantifying the Effect of Sentiment on Information Diffusion in Social Media

Social media have become the main vehicle of information production and consumption online. Millions of users every day log on their Facebook or Twitter accounts to get updates and news, read about their topics of interest, and become exposed to new opportunities and interactions. Although recent studies suggest that the contents users produce will affect the emotions of their readers, we still lack a rigorous understanding of the role and effects of contents sentiment on the dynamics of information diffusion. This work aims at quantifying the effect of sentiment on information diffusion, to understand: (i) whether positive conversations spread faster and/or broader than negative ones (or vice-versa); (ii) what kind of emotions are more typical of popular conversations on social media; and, (iii) what type of sentiment is expressed in conversations characterized by different temporal dynamics. Our findings show that, at the level of contents, negative messages spread faster than positive ones, but positive ones reach larger audiences, suggesting that people are more inclined to share and favorite positive contents, the so-called positive bias. As for the entire conversations, we highlight how different temporal dynamics exhibit different sentiment patterns: for example, positive sentiment builds up for highly-anticipated events, while unexpected events are mainly characterized by negative sentiment. Our contribution is a milestone to understand how the emotions expressed in short texts affect their spreading in online social ecosystems, and may help to craft effective policies and strategies for content generation and diffusion.Comment: 10 pages, 5 figure

The Creation of an Arabic Emotion Ontology Based on E-Motive

© 2017 The Authors. Published by Elsevier B.V. There is an increased interest in social media monitoring to analyse massive, free form, short user-generated text from multiple social media sites such as Facebook, WhatsApp and Twitter. Companies are interested in sentiment analysis to understand customers\u27 opinions about their products/services. Governments and law enforcement agencies are interested in identifying threats to safeguard their country\u27s national security. They are actively seeking ways to monitor and analyse the public\u27s responses to various services, activities and events, especially since social media has become a valuable real-time resource of information. This study builds on prior work that focused on sentiment classification (i.e., positive, negative). This study primarily aims to design and develop a social sentiment-parsing algorithm for capturing and monitoring an extensive and comprehensive range of emotions from Arabic social media text. The study contributes to the field of sentiment analysis (opinion mining) and can subsequently be used for web mining, cleansing and analytics

Gradients of Fear and Anger in the Social Media Response to Terrorism

Research suggests that public fear and anger in wake of a terror attack can each uniquely contribute to policy attitudes and risk-avoidance behaviors. Given the importance of these negative-valanced emotions, there is value in studying how terror events can incite fear and anger at various times and locations relative to an attack. We analyze 36,259 Twitter posts authored in response to the 2016 Orlando nightclub shooting and examined how fear- and anger-related language varied with time and distance from the attack. Fear-related words sharply decreased over time, though the trend was strongest at locations near the attack, while anger-related words slightly decreased over time and increased with distance from Orlando. Comparing these results to users’ pre-attack emotional language suggested that distant users remained both angry and fearful after the shooting, while users close to the attack remained angry but quickly reduced expressions of fear to pre-attack levels