Konten Pengungkapan Perasaan Pelaku Friend With Benefit (FWB) di Media Sosial (Analisis isi akun Instagram @fwb.bercerita)

Instagram @fwb.bercerita is an account that accommodates actors in Friends with Benefits (FWB) relationships to share stories, experiences and feelings anonymously. Through Instagram feed posts, FWB perpetrators reveal various aspects of their relationship, including the feelings of happiness, sadness, disappointment, neutrality that FWB perpetrators feel when they are in a FWB relationship. This research is to describe the extent of the content of messages expressing the feelings of FWB perpetrators in posts on the Instagram account @fwb.bercerita. The concept used in this research is that communication in friendships contains variations in self-disclosure (Devito, 2007). Through social media, communication is not only for finding friends or just posting pictures for entertainment, but can also be a means for sharing information and can even be a medium for expressing feelings for someone or a cathartic medium for channeling their emotions through healthy and non-harmful methods that can reduce stimulation. emotional and a tendency to carry out aggressive attacks against other people (Wahyuningsih, 2017). This research uses a content analysis method with a quantitative approach and descriptive data type. The object of this research is the Instagram feed of the @fwb.bercerita account which is analyzed from the three category structures used, namely the level of expression of feelings, the motive of the FWB perpetrator and the target of the FWB perpetrator. Data collection uses documentation techniques. The results of the research show that of the 10 posts that have been studied, there is a level of expression of feelings referring to feelings of pleasure that is most often felt by FWB perpetrators. Then, the motive for most FWB perpetrators is mutual desire and the targets of most FWB perpetrators are college students. It can be concluded that FWB perpetrators can use the Instagram account @fwb.bercerita as a cathartic medium that accommodates perpetrators in expressing their feelings

A SEMIOTIC ANALYSIS AT INSTAGRAM FILTER

Semiotics has an expression related to sign as a part of human life. In this study,researcher chose the Ferdinand de Saussure Theory to understand the meaning of the signs.Where, in this study, the researcher examined all the signs that appeared in the Instagram filterstarting from the image, sound-imagery, or acoustic imagery, the objects used as signs in thisInstagram video filter. The main aim of this research is to understand the meaning signsconveyed by the creator in the Instagram filter. This research uses a Qualitative research, Theresearcher analyzed data into 3 stages that are data reduction stage, Interpretation stage, andconclusion stage. The finding shows if the most common or commonly found are signifiers in theconcept section, while in the dimensions section they are semantic and pragmatic. It can be saidthat semiotics can be applied and found in various parts of life show personality in representingtheir emotions and memories. So, it can be conclude if humans use signs in their daily lives toshow what they feel and their emotions towards life, both the social environment and so on. Oneof which is through this Instagram filter.Keywords: semiotics, visual sign, instagram filter

What Twitter Profile and Posted Images Reveal About Depression and Anxiety

Previous work has found strong links between the choice of social media images and users' emotions, demographics and personality traits. In this study, we examine which attributes of profile and posted images are associated with depression and anxiety of Twitter users. We used a sample of 28,749 Facebook users to build a language prediction model of survey-reported depression and anxiety, and validated it on Twitter on a sample of 887 users who had taken anxiety and depression surveys. We then applied it to a different set of 4,132 Twitter users to impute language-based depression and anxiety labels, and extracted interpretable features of posted and profile pictures to uncover the associations with users' depression and anxiety, controlling for demographics. For depression, we find that profile pictures suppress positive emotions rather than display more negative emotions, likely because of social media self-presentation biases. They also tend to show the single face of the user (rather than show her in groups of friends), marking increased focus on the self, emblematic for depression. Posted images are dominated by grayscale and low aesthetic cohesion across a variety of image features. Profile images of anxious users are similarly marked by grayscale and low aesthetic cohesion, but less so than those of depressed users. Finally, we show that image features can be used to predict depression and anxiety, and that multitask learning that includes a joint modeling of demographics improves prediction performance. Overall, we find that the image attributes that mark depression and anxiety offer a rich lens into these conditions largely congruent with the psychological literature, and that images on Twitter allow inferences about the mental health status of users.Comment: ICWSM 201

Fashion Conversation Data on Instagram

The fashion industry is establishing its presence on a number of visual-centric social media like Instagram. This creates an interesting clash as fashion brands that have traditionally practiced highly creative and editorialized image marketing now have to engage with people on the platform that epitomizes impromptu, realtime conversation. What kinds of fashion images do brands and individuals share and what are the types of visual features that attract likes and comments? In this research, we take both quantitative and qualitative approaches to answer these questions. We analyze visual features of fashion posts first via manual tagging and then via training on convolutional neural networks. The classified images were examined across four types of fashion brands: mega couture, small couture, designers, and high street. We find that while product-only images make up the majority of fashion conversation in terms of volume, body snaps and face images that portray fashion items more naturally tend to receive a larger number of likes and comments by the audience. Our findings bring insights into building an automated tool for classifying or generating influential fashion information. We make our novel dataset of {24,752} labeled images on fashion conversations, containing visual and textual cues, available for the research community.Comment: 10 pages, 6 figures, This paper will be presented at ICWSM'1

Engineering affect: emotion regulation, the internet, and the techno-social niche

Philosophical work exploring the relation between cognition and the Internet is now an active area of research. Some adopt an externalist framework, arguing that the Internet should be seen as environmental scaffolding that drives and shapes cognition. However, despite growing interest in this topic, little attention has been paid to how the Internet influences our affective life — our moods, emotions, and our ability to regulate these and other feeling states. We argue that the Internet scaffolds not only cognition but also affect. Using various case studies, we consider some ways that we are increasingly dependent on our Internet-enabled “techno-social niches” to regulate the contours of our own affective life and participate in the affective lives of others. We argue further that, unlike many of the other environmental resources we use to regulate affect, the Internet has distinct properties that introduce new dimensions of complexity to these regulative processes. First, it is radically social in a way many of these other resources are not. Second, it is a radically distributed and decentralized resource; no one individual or agent is responsible for the Internet’s content or its affective impact on users. Accordingly, while the Internet can profoundly augment and enrich our affective life and deepen our connection with others, there is also a distinctive kind of affective precarity built into our online endeavors as well

'Yes We Vote': Civic mobilisation and impulsive engagement on Instagram.

Social media have become increasingly central to civic mobilisation and protest movements around the world. Emotions, symbols, self-presentation and visual communication are emerging as key components of networked individualism and connective action by affective publics challenging established political norms. These emerging repertoires have the potential to reignite civic engagement, although their coherence and sustainability have been questioned. We explore these phenomena through an examination of Instagram use during the 2014 Romanian presidential election. Voting irregularities during the 1st round, particularly affecting the diaspora, gave rise to an impulsive civic movement utilising social media to express solidarity and drive turnout in the 2nd round. Using an original coding framework, we look at how narratives of identity, community and engagement were visually constructed by users on Instagram; the activities, settings, spaces, objects and emotions that comprised this multi-authored story. Our analysis reveals the creation of a loose “me too” collective: an emotionally charged hybrid of self-presentation and participation in a shared moment of historic significance, which otherwise lacked particular norms, political agendas or hierarchies. The civic movement on Instagram materialised primarily through photos documenting the act of voting; an imagined community that combined co-presence in physical space with virtual solidarity through photos of ballots, flags and landmarks. The platform appears to favour impulsive, symbolic and affective expression rather than rational or critical dialogue. As in other cases of post-systemic grassroots engagement, individuals came together for a short period of time and expressed the need for change, although this remained largely an abstract signifier

Facial Expression Recognition from World Wild Web

Recognizing facial expression in a wild setting has remained a challenging task in computer vision. The World Wide Web is a good source of facial images which most of them are captured in uncontrolled conditions. In fact, the Internet is a Word Wild Web of facial images with expressions. This paper presents the results of a new study on collecting, annotating, and analyzing wild facial expressions from the web. Three search engines were queried using 1250 emotion related keywords in six different languages and the retrieved images were mapped by two annotators to six basic expressions and neutral. Deep neural networks and noise modeling were used in three different training scenarios to find how accurately facial expressions can be recognized when trained on noisy images collected from the web using query terms (e.g. happy face, laughing man, etc)? The results of our experiments show that deep neural networks can recognize wild facial expressions with an accuracy of 82.12%