Emotions In Viewers Research Articles

Decoding viewer emotions in video ads

Understanding and predicting viewers’ emotional responses to videos has emerged as a pivotal challenge due to its multifaceted applications in video indexing, summarization, personalized content recommendation, and effective advertisement design. A major roadblock in this domain has been the lack of expansive datasets with videos paired with viewer-reported emotional annotations. We address this challenge by employing a deep learning methodology trained on a dataset derived from the application of System1’s proprietary methodologies on over 30,000 real video advertisements, each annotated by an average of 75 viewers. This equates to over 2.3 million emotional annotations across eight distinct categories: anger, contempt, disgust, fear, happiness, sadness, surprise, and neutral, coupled with the temporal onset of these emotions. Leveraging 5-second video clips, our approach aims to capture pronounced emotional responses. Our convolutional neural network, which integrates both video and audio data, predicts salient 5-second emotional clips with an average balanced accuracy of 43.6%, and shows particularly high performance for detecting happiness (55.8%) and sadness (60.2%). When applied to full advertisements, our model achieves a strong average AUC of 75% in determining emotional undertones. To facilitate further research, our trained networks are freely available upon request for research purposes. This work not only overcomes previous data limitations but also provides an accurate deep learning solution for video emotion understanding.

The Role of Networked Narratives in Amplifying or Mitigating Intergroup Prejudice: A YouTube Case Study

This purpose of this research is to understand the role of networked narratives in social media in modulating viewer prejudice toward ethnic neighborhoods. We designed experimental videos on YouTube based on intergroup contact theory and narrative frameworks aimed at (1) gaining knowledge, (2) reducing anxiety, and (3) fostering empathy. Despite consistent storytelling across the videos, we observed significant variations in viewer emotions, especially in replies to comments. We hypothesized that these discrepancies could be explained by the influence of the surrounding digital network on the narrative’s reception. Two-stage research was conducted to understand this phenomenon. First, automated emotion analysis on user comments was conducted to identify the varying emotions. Then, we explored contextual factors surrounding each video on YouTube, focusing on algorithmic curation inferred from traffic sources, region, and search keywords. Findings revealed that negative algorithmic curation and user interactivity result in overall negative viewer emotion, largely driven by video placement and recommendations. However, videos with higher traffic originating from viewers who had watched the storyteller’s other videos result in more positive sentiments and longer visits. This suggests that consistent exposure within the channel can foster more positive acceptance of cultural outgroups by building trust and reducing anxiety. There is the need, then, for storytellers to curate discussions to mitigate prejudice in digital contexts.

Analyzing the packaging design evaluation based on image emotion perception computing

Nowadays, a wide variety of labels of items are widely available, and human consumption is increasingly tailored to meet their individual needs. So, many businesses are starting to focus on improving the functionality of modern packaging. Sensorial paradigms and emotional reactions could change during the user-product interaction lifecycle. The designer's emotional imagination and past experiences are the backbone of conventional product package design, which has limitations due to unmanageable content and an absence of professional advice—the majority of previous research on emotional image analysis aimed to forecast the most common viewer emotions. Since the feelings a picture evokes are quite individual and vary from viewer to viewer, this overarching feeling isn't always enough for practical uses. The research presented an approach to packaging design evaluation based on image emotion perception computing (PDE-IEPC), which combines emotion perception technology with a deep LSTM (Long short-term model), resulting in an immersive and dynamic experience for the human senses. Emotion Perception Computing's Dynamic Multi-task Hypergraph Learning (DMHL) approach considers graphical data, social context, spatial evolution, and location, among other criteria, to evaluate packaging designs efficiently based on their emotional impact. Image-Emotion-Social-Net is a large dataset used to evaluate multidimensional and categorical attitude representation. The dataset is sourced from Flickr and contains over 1 million images presented by over 9000 users. Personalized emotion categorization is an area where research on this dataset shows that the suggested strategy outperforms many modern techniques. The experimental results show that the proposed method achieves a high packaging design quality rate of 94.1 %, a performance success rate of 97.5 %, and a mean square error rate of 2 % compared to other existing methods.

The impact of toxic trolling comments on anti-vaccine YouTube videos

Anti-vaccine trolling on video-hosting websites hinders efforts to increase vaccination rates by using toxic language and threatening claims to intimidate people and promote vaccine hesitancy. However, there is a shortage of research investigating the effects of toxic messages on these platforms. This study focused on YouTube anti-vaccine videos and examined the relationship between toxicity and fear in the comment section of these videos. We discovered that highly liked toxic comments were associated with a significant level of fear in subsequent comments. Moreover, we found complex patterns of contagion between toxicity and fear in the comments. These findings suggest that initial troll comments can evoke negative emotions in viewers, potentially fueling vaccine hesitancy. Our research bears essential implications for managing public health messaging and online communities, particularly in moderating fear-mongering messages about vaccines on social media.

The Power of Emotional Engagement: Tamil Television Serials as a Trail to Enhance Well-being and Successful Ageing among Older Adults

Tamil television serials have the potential to elicit a wide variety of emotions in viewers. Emotional control and expression change as individuals age, and this change is interrelated in various ways. Positive or negative emotions, when put into practice by regulating the emotions associated with health, can result in an increase or decrease in lifespan during successful ageing. This study explores the relationship between emotional well-being and prosperous ageing in Tamil television serials among older adults. An exploratory method was adopted to understand the connection between Tamil television serials and emotional well-being for successful ageing in Tamil Nadu (a southern state of India). Fourteen older adults over 50 who watch Tamil television mega-serials from Tamil Nadu were recruited for in-depth interviews. Three themes emerged from the thematic analysis: emotional connection, life satisfaction, fortitude and ageism among older adults. The findings suggest that Tamil television serials provide emotional engagement, companionship and life satisfaction for older adults, contributing to successful ageing.

Understanding the effect of group emotions on consumer instant order cancellation behavior in livestreaming E-commerce: Empirical evidence from TikTok

Livestreaming e-commerce (LSE) exhibits a noteworthy idiosyncrasy in the form of escalating instant order cancellations, wherein consumers swiftly revoke their purchases during livestreaming before receiving the products. This study leveraged the emotional contagion theory to construct an econometric model and investigate the factors influencing viewers' instant order cancellation behaviors. An extensive dataset was assembled, comprising 4984 product introduction video segments from 768 TikTok livestreaming rooms in China, along with 1.29 million bullet screen comments and 513,551 viewers' information. Employing advanced multimodal data processing technologies, the study extracted anchors' (livestreaming hosts) multimodal emotion features. The empirical findings demonstrate the significant impact of anchors' emotions, encompassing body movements, facial expressions, voices, and conversation contents, on the arousal and valence of group emotions, where the latter exhibit opposing effects on instant order cancellation behavior. This research establishes the paramount roles of anchor and group emotions in viewers' instant order cancellation behaviors, lending credence to the epistemological applicability of emotional contagion theory in the context of LSE. The study's findings offer LSE practitioners practical implications to enhance business decision-making, fostering more engaging and emotionally resonant livestreaming experiences to reduce cancellations and improve customer retention.

Film Genres and Emotions: Genres Watched by Netflix Türkiye Viewers and Emotion Variations During the Pandemic

A dynamic relationship exists between a film/series and the viewer, and genres have an essential place in this relationship. Significantly, the experience of watching films and television series, which is gradually changing, also provides more detail regarding this relationship. Since the COVID-19 pandemic in 2020, the viewing experience has changed, primarily through digital platforms. However, the viewer's tendency to use digital platforms provides more specific data. In this study, using data of genres watched on Netflix Türkiye between 2020 and 2021, which was obtained from FlixPatrol, was analyzed in the context of genres and viewer emotions. Türkiye's data for 2020 and 2021 and the full closure period between April 29 and May 14, 2021 were also examined. Furthermore, movie genres were analyzed quarterly and on a general basis, and the viewing rates of genres in the Top 10 were also interpreted. Numerical data were interpreted qualitatively in the context of genre and cognitive theories.

Affective Image Content Analysis: Two Decades Review and New Perspectives.

Images can convey rich semantics and induce various emotions in viewers. Recently, with the rapid advancement of emotional intelligence and the explosive growth of visual data, extensive research efforts have been dedicated to affective image content analysis (AICA). In this survey, we will comprehensively review the development of AICA in the recent two decades, especially focusing on the state-of-the-art methods with respect to three main challenges - the affective gap, perception subjectivity, and label noise and absence. We begin with an introduction to the key emotion representation models that have been widely employed in AICA and description of available datasets for performing evaluation with quantitative comparison of label noise and dataset bias. We then summarize and compare the representative approaches on (1) emotion feature extraction, including both handcrafted and deep features, (2) learning methods on dominant emotion recognition, personalized emotion prediction, emotion distribution learning, and learning from noisy data or few labels, and (3) AICA based applications. Finally, we discuss some challenges and promising research directions in the future, such as image content and context understanding, group emotion clustering, and viewer-image interaction.

Happiness Begets Money: Emotion and Engagement in Live Streaming

Live streaming offers an unprecedented opportunity for content creators (broadcasters) to deliver their content to consumers (viewers) in real time. In a live stream, viewers may send virtual gifts (tips) to the broadcaster and engage with likes and chats free of charge. These activities reflect viewers’ underlying emotion and are likely to be affected by the broadcaster’s emotion. This article examines the role of emotion in interactive and dynamic business settings such as live streaming. To account for the possibility that broadcaster emotion, viewer emotion, and viewer activities influence each other, the authors estimate a panel vector autoregression model on data at the minute level from 1,450 live streams. The results suggest that a happier broadcaster makes the audience happier and begets intensified viewer activities, in particular tips. In addition, broadcasters reciprocate viewer engagement with more smiles. Further analyses suggest that these effects are pronounced only after a live stream has been active for a while, and they manifest only in streams by broadcasters who have more experience, receive more tips, or are more popular in past live streams. These results help platforms and broadcasters optimize marketing interventions such as broadcaster emotion enhancement in live streaming and quantify the financial returns.

Film Mood and Its Quantitative Determinants in Different Types of Scenes

Films elicit emotions in viewers by infusing the story they tell with an affective character or tone-in a word, a mood. Considerable effort has been made recently to develop computational methods to estimate affective content in film. However, these efforts have focused almost exclusively on style-based features while neglecting to consider different scene types separately. In this study, we investigated the quantitative determinants of film mood across scenes classified by their setting and use of sounds. We examined whether viewers could assess film mood directly in terms of hedonic tone, energetic arousal, and tense arousal; whether their mood ratings differed by scene type; and how various narrative and stylistic film attributes as well as low- and high-level computational features related to the ratings. We found that the viewers were adept at assessing film mood, that sound-based scene classification brought out differences in the mood ratings, and that the low- and high-level features related to different mood dimensions. The study showed that computational film mood estimation can benefit from scene type classification and the use of both low- and high-level features. We have made our clip assessment and annotation data as well as the extracted computational features publicly available.

Emotional reactions to dynamic morphed facial expressions: A new method to induce emotional contagion

In the current study, we tested the utility of a new method developed to study emotional contagion (i.e., the transfer of emotional states between people). Inspired by studies on emotional mimicry – a process that has been postulated as one of the main mechanisms leading to emotional contagion, we created a set of videos showing morphed facial expressions of happiness, sadness, and anger. Following exposure to each video, participants rated their emotions. Our findings demonstrated that the videos evoked congruent emotions in viewers, thereby supporting the notion that dynamic morphed facial expressions may be effective “emotionally contagious” stimuli. Additionally, in line with the previous studies and classic theories of emotional contagion, the displays of anger evoked a complementary reaction of fear.

A method for extracting emotion using colors comprise the painting image

Paintings can evoke emotions in viewers. In this paper, we propose a method for extracting emotions from paintings by using the colors that comprise the paintings. The proposed approach is based on a color image scale, which is one of the popular experimental scales focusing on the relation between colors and emotions. We first construct a color combination and emotional word dataset. To this end, we create a color spectrum from the input painting. We then search for the best matching color combination from the dataset, which is most similar to the color spectrum. The best matching color combination is mapped to the corresponding emotional word. Afterward, we extract the emotional word as the emotion evoked by the painting. To evaluate the proposed method, we compared the results of the proposed algorithm to those of a user study on the extraction of emotions from several paintings. Through several experiments, we show that the proposed method exhibits excellent performance with respect to predicting the emotions evoked by a painting. Finally, we propose an image exploration system based on the emotion extraction method mentioned above. In this system, users can explore painting images emotionally coherently.

Generating Timed Artificial Dream Action (TADA) as the Inverse Process of Freud's Interpretation of Dreams

Apart from being an audacious attempt and a masterpiece, Freud's Interpretation of Dreams has provoked controversy since its publication. After the neurological linkage between Rapid Eye Movement(REM) sleep and dreams, many hypotheses are proposed focusing on REM or its accompanied dreams. This paper reviews recent discoveries regarding the neuro-cognitive aspects of sleep, dreaming, and emotions as a dream ingredient. This paper assumes that a dream content is decomposable into a sequence of Timed Artificial Dream Actions (TADAs) and that dream interpretation resembles the ability of an expert system explaining HOW and WHY questions. Freud tried to answer WHY each dream ingredient is being incorporated. The inverse of this process is to answer HOW a TADA is formed, and it is the concern of this paper. Based on dream-contents, an operational model for dream ingredients is proposed. The proposed TADA generator, nicknamed Oneiros, is decomposed into three modules Morpheus, Phantasos and Phobetor. Morpheus is responsible for the lexical processing of memory contents, in order to perform tasks such as extracting objects, emotions associated with objects, and any object alterations. Phobetor is responsible for extracting viewer emotions, and phobetic-specific aspects. Phantasos is responsible for the actual generation of a TADA.

Affective Computing and Applications of Image Emotion Perceptions

Images can convey rich semantics and evoke strong emotions in viewers. The research of my PhD thesis focuses on image emotion computing (IEC), which aims to predict the emotion perceptions of given images. The development of IEC is greatly constrained by two main challenges: affective gap and subjective evaluation. Previous works mainly focused on finding features that can express emotions better to bridge the affective gap, such as elements-of-art based features and shape features. According to the emotion representation models, including categorical emotion states (CES) and dimensional emotion space (DES), three different tasks are traditionally performed on IEC: affective image classification, regression and retrieval. The state-of-the-art methods on the three above tasks are image-centric, focusing on the dominant emotions for the majority of viewers. For my PhD thesis, I plan to answer the following questions: (1) Compared to the low-level elements-of-art based features, can we find some higher level features that are more interpretable and have stronger link to emotions? (2) Are the emotions that are evoked in viewers by an image subjective and different? If they are, how can we tackle the user-centric emotion prediction? (3) For image-centric emotion computing, can we predict the emotion distribution instead of the dominant emotion category?

A Critical and Semiotic Approach to the Wonderful, Horrible Life Cycle of the Kony 2012 Viral Video

Is it possible or plausible to represent horror and evil persuasively or authentically in these internet-multi-distributed times? And how can we account for a vast, belligerent reaction of public opinion when the representation of horror or evil is watched by an unprecedented, massive amount of people in North America and elsewhere in the YouTube realm? The unparalleled audience success of an unusually lengthy audiovisual narrative uploaded on YouTube whose subject matter is the quest for justice in East Africa was as remarkable as the diverse audience response of dismay, hope, joy and anger it elicited. The reaction was expressed in traditional print media (e.g. a special issue of The New York Times), in countless blogs and in YouTube – through assorted video-responses and written remarks, many of which were so disparaging that this function was disabled for the Kony 2012 video on YouTube. To try to account for the outpour of supportive viewers and of an increasingly negative response, I analyse its visual rhetoric and also some the critical remarks it triggered. The main strategy of the video consists in what I have described elsewhere as the “index appeal” of popular factuality programming (reality shows, docudramas, talk shows and documentaries), namely, the prevalence of allegedly involuntary signs aimed at producing intense emotions in viewers. Peirce’s semiotic theory of indexicality – as well as of iconic and of symbolic signs – is central to my analytical approach, as well as his critique of dualism. I also revisit a 1948 paper of two seminal figures in the pantheon of communication theory, P. Lazarsfeld and R. Merton. Their functionalist analysis of media effects posits a peculiar “narcotizing dysfunction” to account for the apathy produced in the audience despite the increasing intake of media information by the population. This paradoxical media effect posited by early functionalism, I think, is akin to what is harshly criticized over sixty years later about the significant impact produced by the Kony 2012 video on its vast public. Through the case study of a visual media narrative that gathered an audience as large as a populous nation in less than a week, and an equally impressive array of biting critical views in both traditional and new media, the article aims to account for its remarkable success and its ulterior proclaimed failure as a humanitarian campaign in the streets. I will do so by revisiting the early functionalist critique of mass media effects within the analytical framework of the action of indexical-iconic signs in the age of YouTube.

A Critical and Semiotic Approach to the Wonderful, Horrible Life Cycle of the Kony 2012 Viral Video

Is it possible or plausible to represent horror and evil persuasively or authentically in these internet-multi-distributed times? And how can we account for a vast, belligerent reaction of public opinion when the representation of horror or evil is watched by an unprecedented, massive amount of people in North America and elsewhere in the YouTube realm? The unparalleled audience success of an unusually lengthy audiovisual narrative uploaded on YouTube whose subject matter is the quest for justice in East Africa was as remarkable as the diverse audience response of dismay, hope, joy and anger it elicited. The reaction was expressed in traditional print media (e.g. a special issue of The New York Times), in countless blogs and in YouTube – through assorted video-responses and written remarks, many of which were so disparaging that this function was disabled for the Kony 2012 video on YouTube. To try to account for the outpour of supportive viewers and of an increasingly negative response, I analyse its visual rhetoric and also some the critical remarks it triggered. The main strategy of the video consists in what I have described elsewhere as the “index appeal” of popular factuality programming (reality shows, docudramas, talk shows and documentaries), namely, the prevalence of allegedly involuntary signs aimed at producing intense emotions in viewers. Peirce’s semiotic theory of indexicality – as well as of iconic and of symbolic signs – is central to my analytical approach, as well as his critique of dualism. I also revisit a 1948 paper of two seminal figures in the pantheon of communication theory, P. Lazarsfeld and R. Merton. Their functionalist analysis of media effects posits a peculiar “narcotizing dysfunction” to account for the apathy produced in the audience despite the increasing intake of media information by the population. This paradoxical media effect posited by early functionalism, I think, is akin to what is harshly criticized over sixty years later about the significant impact produced by the Kony 2012 video on its vast public. Through the case study of a visual media narrative that gathered an audience as large as a populous nation in less than a week, and an equally impressive array of biting critical views in both traditional and new media, the article aims to account for its remarkable success and its ulterior proclaimed failure as a humanitarian campaign in the streets. I will do so by revisiting the early functionalist critique of mass media effects within the analytical framework of the action of indexical-iconic signs in the age of YouTube.

The multimodal representation of emotion in film: Integrating cognitive and semiotic approaches

This study provides a semiotic theorization of how emotion is represented in film to complement the cognitive approach, which focuses on how film elicits emotion from viewers. Drawing upon social semiotic theories and cognitive theories of emotion, we develop a multimodal framework in which filmic representation of emotion is seen as combinations of semiotic choices derived from cognitive components of emotion. The semiotic model is employed to investigate how emotive meaning is realized through verbal and nonverbal resources. At the discursive level of film, the choices available in the shot organization of eliciting condition and expression are examined. The paper demonstrates how the social semiotic approach, combined with cognitive theory of emotion structure, is able to provide a comprehensive theoretical account of how various film techniques represent emotion. It is also significant for the study of viewer emotion, which to a large degree stems from character emotion.

Beauty, Beasts, and Burlas

The importance of iconography as an art of resistance in contemporary social movements was manifested in the 2006 strike by members of Oaxaca’s Local 22 of Mexico’s National Union of Education Workers. Striking teachers used iconography that incorporated irony, poignant images, and cultural icons such as La Tehuana in presenting their demands for salary increments and a change in government. Ethnographic data collected in Oaxaca since 2006 allow an exploration of the various interpretations of these images. The images were an important visual component of the action, and there is little question that they stirred emotions in viewers, dividing public opinion between those who supported the movement and those who criticized the teachers for hurting Oaxaca’s children and the state’s economy.La importancia de la iconografia como un arte de resistencia en los movimientos sociales contemporaneos se manifesto en 2006 durante la huelga de los miembros oaxaquenos del Seccion 22 del Sindicato Nacional de Trabajador...

擬似同期を用いた動画共有によるビデオ視聴者の感情高揚

擬似同期を用いた動画共有の代表的な例として，ニコニコ動画がある．そこでは，コメントが挿入されたビデオを見ている視聴者は一人でビデオを見ているにもかかわらず，ユーザーとそのシーンの感情をあたかも共有しているかのように感じる．本論文では，擬似同期を用いた動画共有において動画に重畳されるコメントに起因する視聴者の感情高揚について述べる．まず，ラッセルの円環モデルの４感情を生起させる動画を選出する．次に，それらをニコニコ動画に投稿し，それら動画に重畳された代表的なコメントを抽出する．さらに，それらコメントを先の動画に重畳した動画を作成し，被験者がコメントあり動画およびなし動画（オリジナル動画）をそれぞれ視聴しているときの感情の強さを測定する実験を行う．最後に，実験結果より各感情におけるコメントの有無と感情高揚との関係を明らかにするとともに感情心理学や神経科学の観点からその現象を考察する．

The Liberation of Emilia

“So speaking as I think, I die, I die.” (Othello 5.2.248) No episode in Shakespeare’s tragedies is more shocking and more heart-rending than the murder of Desdemona, an event ‘too dreadful to be endured’. From the Renaissance to the present, the dastardliness of this excruciating spectacle has evoked strong emotions in viewers, readers and critics alike. “What instruction can we make out of this Catastrophe?” Thomas Rymer (141) asked in 1693: “Is not this to envenome and sour our spirits, to make us repine and grumble at Providence and the government of the World? If this be our end, what boots it to be Vertuous?” (142).