Content Description on a Mobile Image Sharing Service: Hashtags on Instagram

The mobile social networking application Instagram is a well-known platform for sharing photos and videos. Since it is folksonomy-oriented, it provides the possibility for image indexing and knowledge representation through the assignment of hashtags to posted content. The purpose of this study is to analyze how Instagram users tag their pictures regarding different kinds of picture and hashtag categories. For such a content analysis, a distinction is made between Food, Pets, Selfies, Friends, Activity, Art, Fashion, Quotes (captioned photos), Landscape, and Architecture image categories as well as Content-relatedness (ofness, aboutness, and iconology), Emotiveness, Isness, Performativeness, Fakeness, "Insta"-Tags, and Sentences as hashtag categories. Altogether, 14,649 hashtags of 1,000 Instagram images were intellectually analyzed (100 pictures for each image category). Research questions are stated as follows: RQ1: Are there any differences in relative frequencies of hashtags in the picture categories? On average the number of hashtags per picture is 15. Lowest average values received the categories Selfie (average 10.9 tags per picture) and Friends (average 11.7 tags per picture); for highest, the categories Pet (average 18.6 tags), Fashion (average 17.6 tags), and Landscape (average 16.8 tags). RQ2: Given a picture category, what is the distribution of hashtag categories; and given a hashtag category, what is the distribution of picture categories? 60.20% of all hashtags were classified into the category Content-relatedness. Categories Emotiveness (about 4.38%) and Sentences (0.99%) were less often frequent. RQ3: Is there any association between image categories and hashtag categories? A statistically significant association between hashtag categories and image categories on Instagram exists, as a chi-square test of independence shows. This study enables a first broad overview on the tagging behavior of Instagram users and is not limited to a specific hashtag or picture motive, like previous studies

A Guide to Understanding & Using Folksonomies

In the digital landscape, social media and social platforms have become predominant communication tools in the wider context of Web 2.0. Folksonomies stem from user created metadata that popularized with the evolution of the world wide web in the late 1990s through link-focused websites called weblogs (Spiteri, 2006). Overtime metadata evolved to include tagging, also known as social tagging, hash tagging, and collaborative tagging (Ruslan, 2018). For the final project, I chose to focus on creating an instructional guide that introduced average social media users to the concept of folksonomies. The guide also touches on the specific characteristics of folksonomies (advantages and limitations), how folksonomies operate in the social media landscape, and the impacts folksonomies have on search behaviour in a social landscape

Linguistic, multimodal and cultural code-meshing: Exploring adolescents’ language and literacy practices in social networking sites

This thesis will explore language and literacy practices in social networking sites (SNSs) that both draw on and expand beyond traditional principles of composition. Particularly, it will examine how adolescent participants are engaging with SNSs in ways that extend their learning and life opportunities beyond what is typically accessible in their rural province of Chiang Rai. Despite considerable research on language and literacy, there remains a limited body of research focused on adolescent literacy in Thailand and in rural contexts, such as Chiang Rai. There is also limited research in this area that provides a combined framework to account for the social, cultural, multimodal and linguistic repertoires of adolescents as materialised in their SNS practices. This thesis will draw from sociolinguistic and sociocultural theories of Systemic Functional Linguistics (SFL) and intertextuality to analyse adolescent participants’ linguistic and multimodal texts and how they shape and are shaped by a range of discourses in SNSs. However, both of these theories cannot provide a systematic account of adolescent participants’ multimodal texts in depth. Therefore, this thesis will also draw from multimodality as an analytical framework to account for participants’ multimodal texts (e.g., images, colours and layout). As key findings will demonstrate, the complexity of participants’ language and literacy practices in SNSs involves the blending of not only different languages and modes but also cultural resources (e.g., textual conventions and genres) – or what I refer to as linguistic, multimodal and cultural code-meshing practices. This study will set out a critical perspective on how such practices on SNSs are shaped by Chiang Rai adolescents to make new kinds of meanings, negotiate identities and relationships, and establish belongingness within both local and transnational SNS communities. Evidence from empirical data collected will include surveys and online observations

Searchable Signatures: Context and the Struggle for Recognition

Social networking sites made possible through Web 2.0 allow for unique user-generated tags called “searchable signatures.” These tags move beyond the descriptive and act as means for users to assert online individual and group identities. This paper presents a study of searchable signatures on the Instagram application, demonstrating that these types of tags are valuable not only because they allow for both individuals and groups to engage in what social theorist Axel Honneth calls the “struggle for recognition,” but also because they provide contextual use data and sociohistorical information so important to the understanding of digital objects. Methods for the gathering and display of searchable signatures in digital library environments are also explored

Captured and captioned: Representing family life on Instagram

This article examines how practices of family photography are being transformed in the digital sphere, specifically on Instagram. While research on ‘digital intimacies’ focusses on romantic or peer interactions, the digital practices of families – especially intergenerational interactions – remain understudied. We use Janet Finch’s notion of ‘family display’ to consider how Instagram affords new modes of performing and sharing family life. This concept has exciting potential for media-rich online spaces, but so far, only a few studies examine how social media platforms extend the display of family practices. To explore family photography on Instagram, we analyse a sample of 200 Instagram posts. We argue that features specific to photo-sharing in digital spaces, such as hashtags, emojis and captions, open up new aspects of and audiences for family display. Our analysis paves the way for future research about how relationships are displayed across a range of digital platforms.info:eu-repo/semantics/publishedVersio

Learning to Hash-tag Videos with Tag2Vec

User-given tags or labels are valuable resources for semantic understanding of visual media such as images and videos. Recently, a new type of labeling mechanism known as hash-tags have become increasingly popular on social media sites. In this paper, we study the problem of generating relevant and useful hash-tags for short video clips. Traditional data-driven approaches for tag enrichment and recommendation use direct visual similarity for label transfer and propagation. We attempt to learn a direct low-cost mapping from video to hash-tags using a two step training process. We first employ a natural language processing (NLP) technique, skip-gram models with neural network training to learn a low-dimensional vector representation of hash-tags (Tag2Vec) using a corpus of 10 million hash-tags. We then train an embedding function to map video features to the low-dimensional Tag2vec space. We learn this embedding for 29 categories of short video clips with hash-tags. A query video without any tag-information can then be directly mapped to the vector space of tags using the learned embedding and relevant tags can be found by performing a simple nearest-neighbor retrieval in the Tag2Vec space. We validate the relevance of the tags suggested by our system qualitatively and quantitatively with a user study

Hashtags on Instagram: Self-created or Mediated by Best Practices and Tools?

Social media enables conversations mediated through documents as texts, audio, images, or videos. Likewise, hashtags became an essential medium for social media communication. Instagram is well-known as one of the current platforms for hashtagging. This exploratory study investigates how hashtags used on Instagram became established in respect of self-creation and best practices or tools. The analysis is based on data obtained from an online survey (N = 1,006) of Instagram users. 55.7% of the respondents use hashtags on Instagram. Only self-created hashtags are assigned by 41.4%, whereas 58.6% are (sometimes) inspired by others. Best practices and tools based on friends/other users or Instagram functions are more frequently used in contrast to offers from influencers or third-parties (e.g. guides, hashtag-sets). Furthermore, the majority does not intentionally use false hashtags. This study enables a first overview of the Instagram users’ hashtagging creation behavior and selection process

Scalable Privacy-Compliant Virality Prediction on Twitter

The digital town hall of Twitter becomes a preferred medium of communication for individuals and organizations across the globe. Some of them reach audiences of millions, while others struggle to get noticed. Given the impact of social media, the question remains more relevant than ever: how to model the dynamics of attention in Twitter. Researchers around the world turn to machine learning to predict the most influential tweets and authors, navigating the volume, velocity, and variety of social big data, with many compromises. In this paper, we revisit content popularity prediction on Twitter. We argue that strict alignment of data acquisition, storage and analysis algorithms is necessary to avoid the common trade-offs between scalability, accuracy and privacy compliance. We propose a new framework for the rapid acquisition of large-scale datasets, high accuracy supervisory signal and multilanguage sentiment prediction while respecting every privacy request applicable. We then apply a novel gradient boosting framework to achieve state-of-the-art results in virality ranking, already before including tweet's visual or propagation features. Our Gradient Boosted Regression Tree is the first to offer explainable, strong ranking performance on benchmark datasets. Since the analysis focused on features available early, the model is immediately applicable to incoming tweets in 18 languages.Comment: AffCon@AAAI-19 Best Paper Award; Presented at AAAI-19 W1: Affective Content Analysi