'The Perfect One'

Jiang, Jialun Aaron; Fiesler, Casey; Brubaker, Jed R.

doi:10.1145/3274349

Cited by 32 publications

(8 citation statements)

References 33 publications

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“…In addition, several studies [39], [40] devised methods to predict viewers' sentiments towards animated GIFs. Despite viewer engagement, in [41], it was concluded that viewers may have diverse interpretations of animated GIFs used in communication. They predicted facial expressions, histograms, and aesthetic features and then compared them to those in [40] to determine the most appropriate video features for expressing useful emotions in GIFs.…”

Section: Animated Gif Generation Methodsmentioning

confidence: 99%

FRC-GIF: Frame Ranking-Based Personalized Artistic Media Generation Method for Resource Constrained Devices

Mujtaba,

Khowaja,

Jarwar

et al. 2024

IEEE Trans. Big Data

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Generating video highlights in the form of animated graphics interchange formats (GIFs) has significantly simplified the process of video browsing. Animated GIFs have paved the way for applications concerning streaming platforms and emerging technologies. Existing studies have led to large computational complexity without considering user personalization. This paper proposes lightweight method to attract users and increase views of videos through personalized artistic media, i.e., static thumbnails and animated GIF generation. The proposed method analyzes lightweight thumbnail containers (LTC) using the computational resources of the client device to recognize personalized events from feature-length sports videos. Next, the thumbnails are then ranked through the frame rank pooling method for their selection. Subsequently, the proposed method processes small video segments rather than considering the whole video for generating artistic media. This makes our approach more computationally efficient compared to existing methods that use the entire video data; thus, the proposed method complies with sustainable development goals. Furthermore, the proposed method retrieves and uses thumbnail containers and video segments, which reduces the required transmission bandwidth as well as the amount of locally stored data. Experiments reveal that the computational complexity of our method is 3.73 times lower than that of the state-of-the-art method.

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Section: Animated Gif Generation Methodsmentioning

confidence: 99%

FRC-GIF: Frame Ranking-Based Personalized Artistic Media Generation Method for Resource Constrained Devices

Mujtaba,

Khowaja,

Jarwar

et al. 2024

IEEE Trans. Big Data

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“…Some mixed-method studies analyze manipulative rhetorics in factoid online articles (Tian et al, 2022), or interpret lexical markers of minority stress experience in LGBTQ+ communities on Reddit (Saha et al, 2019). Qualitative approaches focus on, for example, examining narration and expression of grief and the role of sociotechnical features on TikTok (Eriksson Krutröck, 2021), analyzing potential misunderstandings when using animated graphics interchange formats (GIFs) in nonverbal social media communication (Jiang et al, 2018), or showing how LOLspeak is important for humorous communication in gendered interaction during Hackathons (Brooke, 2022).…”

Section: Background and Related Workmentioning

confidence: 99%

You Can (Not) Say What You Want: Using Algospeak to Contest and Evade Algorithmic Content Moderation on TikTok

Steen,

Yurechko,

Klug

2023

Social Media + Society

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Social media users have long been aware of opaque content moderation systems and how they shape platform environments. On TikTok, creators increasingly utilize algospeak to circumvent unjust content restriction, meaning, they change or invent words to prevent TikTok’s content moderation algorithm from banning their video (e.g., “le$bean” for “lesbian”). We interviewed 19 TikTok creators about their motivations and practices of using algospeak in relation to their experience with TikTok’s content moderation. Participants largely anticipated how TikTok’s algorithm would read their videos, and used algospeak to evade unjustified content moderation while simultaneously ensuring target audiences can still find their videos. We identify non-contextuality, randomness, inaccuracy, and bias against marginalized communities as major issues regarding freedom of expression, equality of subjects, and support for communities of interest. Using algospeak, we argue for a need to improve contextually informed content moderation to valorize marginalized and tabooed audiovisual content on social media.

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“…While new emojis are being continuously designed to represent various objects and events, they primarily focus on representing emotions. Animated GIFs are another popular way of sharing emotions [5,23]. However, animated GIFs mostly focus on funny memes and might not be supportive enough for expressing diverse chat scenarios.…”

Section: Related Work 21 Diversifying Expressions On Mobile Chatsmentioning

confidence: 99%

“…Emojis are good to express emotions and are often used in social communication [26, 27, 30-32, 35, 41, 56, 59]. Memes, or animated GIFs, are popular for trendy and funny images [5,22,23]. Personal images [11,29] are useful in sharing deep personal and social context.…”

Section: Introductionmentioning

confidence: 99%

Messaging Beyond Texts with Real-time Image Suggestions

Kim

Gong

Han

et al. 2020

22nd International Conference on Human-Computer Interaction With Mobile Devices and Services

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While people primarily communicate with text in mobile chat applications, they are increasingly using visual elements such as images, emojis, and memes. Using such visual elements could help users communicate clearly and make chatting experience enjoyable. However, finding and inserting contextually appropriate images during the chat can be both tedious and distracting. We introduce Milli-Cat, a real-time image suggestion system that recommends images that match the chat content within a mobile chat application (i.e., autocomplete with images). MilliCat combines natural language processing (e.g., keyword extraction, dependency parsing) and mobile computing (e.g., resource and energy-efficiency) techniques to autonomously make image suggestions when users might want to use images. Through multiple user studies, we investigated the effectiveness of our design choices, the frequency and motivation of image usage by the participants, and the impact of MilliCat on mobile chat experiences. Our results indicate that MilliCat's realtime image suggestion enables users to quickly and conveniently select and display images on mobile chat by significantly reducing the latency in the image selection process (3.19× improvement) and consequently more frequent image usage (1.8×) than existing solutions. Our study participants reported that they used images more often with MilliCat as the images helped them convey information more effectively, emphasize their opinion, express emotions, and have fun chatting experience. CCS CONCEPTS • Human-centered computing → Interactive systems and tools; Empirical studies in interaction design; Ubiquitous and mobile computing systems and tools.

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'The Perfect One'

Cited by 32 publications

References 33 publications

FRC-GIF: Frame Ranking-Based Personalized Artistic Media Generation Method for Resource Constrained Devices

FRC-GIF: Frame Ranking-Based Personalized Artistic Media Generation Method for Resource Constrained Devices

You Can (Not) Say What You Want: Using Algospeak to Contest and Evade Algorithmic Content Moderation on TikTok

Messaging Beyond Texts with Real-time Image Suggestions

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