Analysis and Decision-Making with Social Media

abstract: The rapid advancements of technology have greatly extended the ubiquitous nature of smartphones acting as a gateway to numerous social media applications. This brings an immense convenience to the users of these applications wishing to stay connected to other individuals through sharing their statuses, posting their opinions, experiences, suggestions, etc on online social networks (OSNs). Exploring and analyzing this data has a great potential to enable deep and fine-grained insights into the behavior, emotions, and language of individuals in a society. This proposed dissertation focuses on utilizing these online social footprints to research two main threads – 1) Analysis: to study the behavior of individuals online (content analysis) and 2) Synthesis: to build models that influence the behavior of individuals offline (incomplete action models for decision-making). A large percentage of posts shared online are in an unrestricted natural language format that is meant for human consumption. One of the demanding problems in this context is to leverage and develop approaches to automatically extract important insights from this incessant massive data pool. Efforts in this direction emphasize mining or extracting the wealth of latent information in the data from multiple OSNs independently. The first thread of this dissertation focuses on analytics to investigate the differentiated content-sharing behavior of individuals. The second thread of this dissertation attempts to build decision-making systems using social media data. The results of the proposed dissertation emphasize the importance of considering multiple data types while interpreting the content shared on OSNs. They highlight the unique ways in which the data and the extracted patterns from text-based platforms or visual-based platforms complement and contrast in terms of their content. The proposed research demonstrated that, in many ways, the results obtained by focusing on either only text or only visual elements of content shared online could lead to biased insights. On the other hand, it also shows the power of a sequential set of patterns that have some sort of precedence relationships and collaboration between humans and automated planners.Dissertation/ThesisDoctoral Dissertation Computer Science 201

Learning Representations of Social Media Users

User representations are routinely used in recommendation systems by platform developers, targeted advertisements by marketers, and by public policy researchers to gauge public opinion across demographic groups. Computer scientists consider the problem of inferring user representations more abstractly; how does one extract a stable user representation - effective for many downstream tasks - from a medium as noisy and complicated as social media? The quality of a user representation is ultimately task-dependent (e.g. does it improve classifier performance, make more accurate recommendations in a recommendation system) but there are proxies that are less sensitive to the specific task. Is the representation predictive of latent properties such as a person's demographic features, socioeconomic class, or mental health state? Is it predictive of the user's future behavior? In this thesis, we begin by showing how user representations can be learned from multiple types of user behavior on social media. We apply several extensions of generalized canonical correlation analysis to learn these representations and evaluate them at three tasks: predicting future hashtag mentions, friending behavior, and demographic features. We then show how user features can be employed as distant supervision to improve topic model fit. Finally, we show how user features can be integrated into and improve existing classifiers in the multitask learning framework. We treat user representations - ground truth gender and mental health features - as auxiliary tasks to improve mental health state prediction. We also use distributed user representations learned in the first chapter to improve tweet-level stance classifiers, showing that distant user information can inform classification tasks at the granularity of a single message.Comment: PhD thesi

Learning Representations of Social Media Users

User representations are routinely used in recommendation systems by platform developers, targeted advertisements by marketers, and by public policy researchers to gauge public opinion across demographic groups. Computer scientists consider the problem of inferring user representations more abstractly; how does one extract a stable user representation - effective for many downstream tasks - from a medium as noisy and complicated as social media? The quality of a user representation is ultimately task-dependent (e.g. does it improve classifier performance, make more accurate recommendations in a recommendation system) but there are proxies that are less sensitive to the specific task. Is the representation predictive of latent properties such as a person's demographic features, socioeconomic class, or mental health state? Is it predictive of the user's future behavior? In this thesis, we begin by showing how user representations can be learned from multiple types of user behavior on social media. We apply several extensions of generalized canonical correlation analysis to learn these representations and evaluate them at three tasks: predicting future hashtag mentions, friending behavior, and demographic features. We then show how user features can be employed as distant supervision to improve topic model fit. Finally, we show how user features can be integrated into and improve existing classifiers in the multitask learning framework. We treat user representations - ground truth gender and mental health features - as auxiliary tasks to improve mental health state prediction. We also use distributed user representations learned in the first chapter to improve tweet-level stance classifiers, showing that distant user information can inform classification tasks at the granularity of a single message.Comment: PhD thesi

Making the FTC ☺: An Approach to Material Connections Disclosures in the Emoji Age

In examining the rise of influencer marketing and emoji’s concurrent surge in popularity, it naturally follows that emoji should be incorporated into the FTC’s required disclosures for sponsored posts across social media platforms. While current disclosure methods the FTC recommends are easily jumbled or lost in other text, using emoji to disclose material connections would streamline disclosure requirements, leveraging an already-popular method of communication to better reach consumers. This Note proposes that the FTC adopts an emoji as a preferred method of disclosure for influencer marketing on social media. Part I discusses the rise of influencer marketing, the FTC and its history of regulating sponsored content, and the current state of regulation. Part II explores the proliferation of emoji as a method of communication, and the role of the Unicode Consortium in regulating the adoption of new emoji. Part III makes the case for incorporating emoji as a method of disclosure to bridge compliance gaps, and offers additional recommendations to increase compliance with existing regulations

Social Media Platforms for Small Fashion Businesses

This major paper examined the effectiveness of social media platforms for small fashion businesses. The social media platforms investigated were Facebook, Instagram, Twitter, Snapchat, and Pinterest. Many factors such as target market, product mix, and budget need to considered. Once these factors are clearly defined, the information in the paper can be applied to the small fashion business

Let Me Take a #Shelfie: An Assemblage Explored Through Framing

As emerging technologies rapidly change the way that individuals socially interact, researchers can look back to older theories of social organization in order to apply traditional concepts to new ways of being. A #shelfie is a popular hashtag used on Instagram, a social media application, typically used with a post that consist of any visual media containing book(s) or item(s) related to literature in a physical space within, around, and/or upon a piece of furniture. This thesis is centered around the examination of a data collection that gathered top #shelfie posts on Instagram for the purpose of visual content and textual analysis. I argue that users are performing and constructing identities using curated #shelfies that actually span beyond the original typical bookshelf content, that this particular content is being utilized mostly by users identifying as women, and that it highlights areas of multiple framing occurring at the same time through Instagram and the objects being posted. My thesis is anchored within a multi-disciplinary framework that utilizes Erving Goffman’s theories of self and framing, cultural materialism as framing from scholars such as Daniel Miller, and that this analysis can be understood in a Deleuzian lens by examining how assemblage theory can help to navigate what #shelfie is, how it was produced, and what it may mean for future studies of digital media and the self. There will be no pixel left unturned

Digital Methods and Technicity-of-the-Mediums. From Regimes of Functioning to Digital Research

Digital methods are taken here as a research practice crucially situated in the technological environment that it explores and exploits. Through software-oriented analysis, this research practice proposes to re-purpose online methods and data for social-medium research but not considered as a proper type of fieldwork because these methods are new and still in their process of description. These methods impose proximity with software and reflect an environment inhabited by technicity. Thus, this dissertation is concerned with a key element of the digital methods research approach: the computational (or technical) mediums as carriers of meaning (see Berry, 2011; Rieder, 2020). The central idea of this dissertation is to address the role of technical knowledge, practise and expertise (as problems and solutions) in the full range of digital methods, taking the technicity of the computational mediums and digital records as objects of study. By focusing on how the concept of technicity matters in digital research, I argue that not only do digital methods open an opportunity for further enquiry into this concept, but they also benefit from such enquiry, since the working material of this research practice are the media, its methods, mechanisms and data. In this way, the notion of technicity-of-the-mediums is used in two senses pointing on the one hand to the effort to become acquainted with the mediums (from a conceptual, technical and empirical perspective), on the other hand, to the object of technical imagination (the capacity of considering the features and practical qualities of technical mediums as ensemble and as a solution to methodological problems). From the standpoint of non-developer researchers and the perspective of software practice, the understanding of digital technologies starts from direct contact, comprehension and different uses of (research) software and the web environment. The journey of digital methods is only fulfilled by technical practice, experimentation and exploration. Two main arguments are put forward in this dissertation. The first states that we can only repurpose what we know well, which means that we need to become acquainted with the mediums from a conceptual-technical-practical perspective; whereas, the second argument states that the practice of digital methods is enhanced when researchers make room for, grow and establish a sensitivity to the technicity-of-the-mediums. The main contribution of this dissertation is to develop a series of conceptual and practical principles for digital research. Theoretically, this dissertation suggests a broader definition of medium in digital methods and introduces the notion of the technicity-of-the-mediums and three distinct but related aspects to consider – namely platform grammatisation, cultures of use and software affordances, as an attempt to defuse some of the difficulties related to the use of digital methods. Practically, it presents concrete methodological approaches providing new analytical perspectives for social media research and digital network studies, while suggesting a way of carrying out digital fieldwork which is substantiated by technical practices and imagination.Os métodos digitais são aqui tomados como uma prática de investigação crucialmente situada no ambiente tecnológico que explora e do qual tira benefício. Esta prática de pesquisa propõe a reorientação dos métodos online e dos dados para a pesquisa social e do meio através da análise orientada por software, prática ainda não considerada como um tipo adequado de trabalho de campo porque estes métodos são novos e a sua descrição está ainda numa fase incipiente. Estes métodos obrigam a adquirir familiaridade com o software e refletem um ambiente habitado pela tecnicidade. Esta dissertação diz assim respeito a um elemento-chave da abordagem de investigação dos métodos digitais: os meios computacionais (ou técnicos) enquanto portadores de significado (ver Berry, 2011; Rieder, 2020). A ideia central desta dissertação é a de refletir sobre o papel do conhecimento técnico, da prática técnica e da aquisição de competências (como problemas e como soluções) em todo o âmbito dos métodos digitais, assumindo a tecnicidade dos meios computacionais e dos registos digitais como objetos de estudo. Ao centrar-me na forma como o conceito de tecnicidade é fundamental na investigação digital, argumento que não só os métodos digitais abrem uma oportunidade para uma investigação mais aprofundada deste conceito, mas também que beneficiam deste tipo de investigação, uma vez que a matéria-prima desta prática de pesquisa são os meios, os seus métodos, mecanismos e dados. Deste modo, a noção de tecnicidade-dos-meios é utilizada em dois sentidos: apontando, por um lado, para a necessidade de conhecimento dos meios (duma perspetiva conceptual, técnica e empírica) e, por outro, para o objeto da imaginação técnica (a capacidade de tomar as características e as qualidades práticas dos meios computacionais como um conjunto [ensemble] e como uma solução para problemas metodológicos). Segundo o ponto de vista dos pesquisadores que não estão familiarizados com o desenvolvimento de software (ou de ferramentas digitais) bem como da perspectiva da prática do software, a compreensão das tecnologias digitais deve partir de um contato direto, da compreensão e dos diferentes usos do software e do ambiente da web. O percurso dos métodos digitais só pode ser concretizado pela prática técnica, pela experimentação e pela exploração. Dois argumentos principais são apresentados nesta dissertação. O primeiro afirma que só podemos tirar proveito daquilo que conhecemos de forma aprofundada, o que significa que é necessário que nos familiarizemos com os meios numa perspetiva conceptual-técnica-prática, enquanto o segundo argumento afirma que a prática dos métodos digitais é aperfeiçoada quando os investigadores estão recetivos a, amadurecem e adquirem uma sensibilidade para a tecnicidade-dos-meios. A principal contribuição desta dissertação é o desenvolvimento de um conjunto de princípios conceptuais e práticos para a pesquisa digital. Teoricamente, esta dissertação propõe uma definição mais ampla de meio nos métodos digitais, introduz o conceito de tecnicidade dos- meios e aponta para três facetas distintas mas relacionadas – referimo-nos à gramatização das plataformas, às culturas de utilização e às affordances do software –, como uma solução para minorar algumas das dificuldades relacionadas com a utilização dos métodos digitais. Na prática, apresenta abordagens metodológicas concretas que fornecem novas perspetivas analíticas para a investigação dos media sociais e para os estudos de redes digitais, ao mesmo tempo que sugere uma forma de levar a cabo trabalho de campo digital que é substanciada por práticas técnicas e pela imaginação técnica

Predicting digital engagement from social media

Undergraduate thesis submitted to the Department of Computer Science and Information Systems, Ashesi University, in partial fulfillment of Bachelor of Science degree in Management Information Systems, May 2022The purpose of this research is to explore the adoption of a predictive machine learning model for business digital engagement. However, the focus aims to test the hypothesis of whether or not machine learning regression models can be used to effectively predict social media engagement metrics. The exploratory research this project undergoes consists of three data analysis experiments, filtered by factors such as the size of the data set and the inclusiveness of outlier data elements. At the start of the project, Instagram and Twitter were both considered as data sources, but revised data privacy policies prevented a data collection process within scope, for Instagram specifically. The following models: Decision Tree Regressor, Random Forest Regressor, Support Vector Regressor and Artificial Neural Network was trained and tested on Twitter data. Of the three experiments conducted, the third experiment consisting of the larger data set and removed outliers proved to be the most effective. Though the predicted results are not accurate enough to be replicated across several edge scenarios.Ashesi Universit