On the Influence of Emotional Valence Shifts on the Spread of Information in Social Networks

Kušen, Ema; Strembeck, Mark; Cascavilla, Giuseppe; Conti, Mauro

doi:10.1145/3110025.3110031

Cited by 19 publications

(11 citation statements)

References 17 publications

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“…According to the ICM model, terror and shooting attacks are characteristic for the initial expression of fear and sadness. Further studies have shown that in the post-event period an initial shock may evolve into anger and a search for a culprit to blame (Guo 2017;Rosas 2015;Tapia et al 2014), as well as the expression of positive emotions and empathy to emotionally bond and support the ones affected by the event (Guo 2017;Kušen et al 2017b;Lin and Margolin 2014).…”

Section: Emotions Expressed During Shootings and Terror Eventsmentioning

confidence: 99%

“…However, not only negative emotions (such as anger and fear) are shared on social media but also a number of positive emotions, such as joy and relief. In fact, prior studies have shown that in negative events positive emotions may even prevail over negative emotions because they help reduce stress, lower the intensity of high arousal emotions, and increase the feelings of hope, gratitude, and compassion, as well as empathy (Guo 2017;Folkman and Moskowitz 2000;Fredrickson et al 2003;Kim and Niederdeppe 2013;Kušen et al 2017b). This human tendency to use positive emotions as an antidote against the effects of negative emotions is referred to as the undoing hypothesis (Fredrickson et al 2000).…”

Section: Introductionmentioning

confidence: 99%

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An analysis of emotion-exchange motifs in multiplex networks during emergency events

Kušen

Strembeck

2019

Appl Netw Sci

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In this paper, we present an analysis of the emotion-exchange patterns that arise from Twitter messages sent during emergency events. To this end, we performed a systematic structural analysis of the multiplex communication network that we derived from a data-set including more than 1.9 million tweets that have been sent during five recent shootings and terror events. In order to study the local communication structures that emerge as Twitter users directly exchange emotional messages, we propose the concept of emotion-exchange motifs. Our findings suggest that emotion-exchange motifs which contain reciprocal edges (indicating online conversations) only emerge when users exchange messages that convey anger or fear, either in isolation or in any combination with another emotion. In contrast, the expression of sadness, disgust, surprise, as well as any positive emotion are rather characteristic for emotion-exchange motifs representing one-way communication patterns (instead of online conversations). Among other things, we also found that a higher structural similarity exists between pairs of network layers consisting of one high-arousal emotion and one low-arousal emotion, rather than pairs of network layers belonging to the same arousal dimension.

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Section: Emotions Expressed During Shootings and Terror Eventsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An analysis of emotion-exchange motifs in multiplex networks during emergency events

Kušen

Strembeck

2019

Appl Netw Sci

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“…Since the advent of the Web 2.0 era, people are publicly expressing their emotions as a form of interpersonal interaction on online social networks (OSNs). Even though prior studies have shown that such sharing of emotions may lead to beneficial effects [1], [2], there are also potential threats arising from emotionally-charged content that is shared via OSNs. In [1], Hidalgo et al assert that online social sharing can influence the individuals' overall well-being and life satisfaction.…”

Section: Introductionmentioning

confidence: 99%

Identifying Emotions in Social Media: Comparison of Word-Emotion Lexicons

Kušen

Cascavilla

Figl

et al. 2017

2017 5th International Conference on Future Internet of Things and Cloud Workshops (FiCloudW)

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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 survey.

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“…Hence, we measured the emotions embedded by different tweets based on the NRC dictionary [40]. As suggested earlier [26], we use a low-dimensional representation: (i) positive emotions refer anticipation, joy, and trust; (ii) negative emotions includes anger, disgust, fear, and sadness; and (iii) surprise was kept as a separate category.…”

Section: Model Variablesmentioning

confidence: 99%

Detecting False Rumors from Retweet Dynamics on Social Media

Naumzik,

Feuerriegel

2022

Preprint

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False rumors are known to have detrimental effects on society. To prevent the spread of false rumors, social media platforms such as Twitter must detect them early. In this work, we develop a novel probabilistic mixture model that classifies true vs. false rumors based on the underlying spreading process. Specifically, our model is the first to formalize the self-exciting nature of true vs. false retweeting processes. This results in a novel mixture marked Hawkes model (MMHM). Owing to this, our model obviates the need for feature engineering; instead, it directly models the spreading process in order to make inferences of whether online rumors are incorrect. Our evaluation is based on 13,650 retweet cascades of both true. vs. false rumors from Twitter. Our model recognizes false rumors with a balanced accuracy of 64.97 % and an AUC of 69.46 %. It outperforms state-of-the-art baselines (both neural and feature engineering) by a considerable margin but while being fully interpretable. Our work has direct implications for practitioners: it leverages the spreading process as an implicit quality signal and, based on it, detects false content. CCS CONCEPTS• Information systems → Social networks; Data mining; • Mathematics of computing → Stochastic processes.

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On the Influence of Emotional Valence Shifts on the Spread of Information in Social Networks

Cited by 19 publications

References 17 publications

An analysis of emotion-exchange motifs in multiplex networks during emergency events

An analysis of emotion-exchange motifs in multiplex networks during emergency events

Identifying Emotions in Social Media: Comparison of Word-Emotion Lexicons

Detecting False Rumors from Retweet Dynamics on Social Media

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