Using Social Media Websites to Support Scenario-Based Design of Assistive Technology

Indiana University-Purdue University Indianapolis (IUPUI)Having representative users, who have the targeted disability, in accessibility studies is vital to the validity of research findings. Although it is a widely accepted tenet in the HCI community, many barriers and difficulties make it very resource-demanding for accessibility researchers to recruit representative users. As a result, researchers recruit non-representative users, who do not have the targeted disability, instead of representative users in accessibility studies. Although such an approach has been widely justified, evidence showed that findings derived from non-representative users could be biased and even misleading. To address this problem, researchers have come up with different solutions such as building pools of users to recruit from. But still, the data is not widely available and needs a lot of effort and resource to build and maintain. On the other hand, online social media websites have become popular in the last decade. Many online communities have emerged that allow online users to discuss health-related subjects, exchange useful information, or provide emotional support. A large amount of data accumulated in such online communities have gained attention from researchers in the healthcare domain. And many researches have been done based on data from social media websites to better understand health problems to improve the wellbeing of people. Despite the increasing popularity, the value of data from social media websites for accessibility research remains untapped. Hence, my work aims to create methods that could extract valuable information from data collected on social media websites for accessibility practitioners to support their design process. First, I investigate methods that enable researchers to effectively collect representative data from social media websites. More specifically, I look into machine learning approaches that could allow researchers to automatically identify online users who have disabilities (representative users). Second, I investigate methods that could extract useful information from user-generated free-text using techniques drawn from the information extraction domain. Last, I explore how such information should be visualized and presented for designers to support the scenario-based design process in accessibility studies

Beyond Words: Analyzing Social Media with Text and Images

People express their opinions and experiences through text and images in social media platforms. Analyzing social media content has several applications in natural language processing such as sentiment analysis, hate speech detection, fact checking and sarcasm detection. Combining text and images from social media posts is challenging due to weak visual-text relationships. For instance, a post with the text: Feeling on top of the world after acing my final exams! and a picture of a group of friends at the beach. The image and the text are weakly related as the image does not directly align with the academic context, potentially leading to confusion or misinterpretation of the intended message. Thus, effectively modeling text and images from social media posts is crucial for advancing natural language understanding. This thesis proposes a number of new challenging multimodal classification tasks: point-of-interest (POI) type prediction, political advertisements analysis, and influencer content analysis. First, we introduce POI type prediction which consists of inferring the type of location from which a social media message was posted such as a park or a restaurant. This task is relevant to study a place's identity and has applications such as POI visualization and recommendation. Second, we analyze political advertisements by introducing two new datasets containing political ads labeled by the sponsor's ideology (conservative, liberal), and the sponsor type (political party, third party); and we experiment with multimodal models for advertisement classification. Analyzing political ads is important for researching the characteristics of online campaigns (e.g. voter targeting, non-party campaigns and misinformation) on a large scale. Next, we perform an extensive analysis of influencer content including multimodal approaches for identifying commercial posts, i.e., content that is monetized. Automatically detecting influencer commercial posts is of utmost importance for addressing issues related to transparency and regulatory compliance, such as misleading advertising. Finally, this thesis also presents novel methods for tackling the challenges of modeling text and visual content in social media. We propose two auxiliary losses, Image-Text Contrastive which encourages the model to capture the underlying dependencies in multimodal posts; and Image-Text Matching to enable visual and language alignment

Exploring the value of big data analysis of Twitter tweets and share prices

Over the past decade, the use of social media (SM) such as Facebook, Twitter, Pinterest and Tumblr has dramatically increased. Using SM, millions of users are creating large amounts of data every day. According to some estimates ninety per cent of the content on the Internet is now user generated. Social Media (SM) can be seen as a distributed content creation and sharing platform based on Web 2.0 technologies. SM sites make it very easy for its users to publish text, pictures, links, messages or videos without the need to be able to program. Users post reviews on products and services they bought, write about their interests and intentions or give their opinions and views on political subjects. SM has also been a key factor in mass movements such as the Arab Spring and the Occupy Wall Street protests and is used for human aid and disaster relief (HADR). There is a growing interest in SM analysis from organisations for detecting new trends, getting user opinions on their products and services or finding out about their online reputation. Companies such as Amazon or eBay use SM data for their recommendation engines and to generate more business. TV stations buy data about opinions on their TV programs from Facebook to find out what the popularity of a certain TV show is. Companies such as Topsy, Gnip, DataSift and Zoomph have built their entire business models around SM analysis. The purpose of this thesis is to explore the economic value of Twitter tweets. The economic value is determined by trying to predict the share price of a company. If the share price of a company can be predicted using SM data, it should be possible to deduce a monetary value. There is limited research on determining the economic value of SM data for “nowcasting”, predicting the present, and for forecasting. This study aims to determine the monetary value of Twitter by correlating the daily frequencies of positive and negative Tweets about the Apple company and some of its most popular products with the development of the Apple Inc. share price. If the number of positive tweets about Apple increases and the share price follows this development, the tweets have predictive information about the share price. A literature review has found that there is a growing interest in analysing SM data from different industries. A lot of research is conducted studying SM from various perspectives. Many studies try to determine the impact of online marketing campaigns or try to quantify the value of social capital. Others, in the area of behavioural economics, focus on the influence of SM on decision-making. There are studies trying to predict financial indicators such as the Dow Jones Industrial Average (DJIA). However, the literature review has indicated that there is no study correlating sentiment polarity on products and companies in tweets with the share price of the company. The theoretical framework used in this study is based on Computational Social Science (CSS) and Big Data. Supporting theories of CSS are Social Media Mining (SMM) and sentiment analysis. Supporting theories of Big Data are Data Mining (DM) and Predictive Analysis (PA). Machine learning (ML) techniques have been adopted to analyse and classify the tweets. In the first stage of the study, a body of tweets was collected and pre-processed, and then analysed for their sentiment polarity towards Apple Inc., the iPad and the iPhone. Several datasets were created using different pre-processing and analysis methods. The tweet frequencies were then represented as time series. The time series were analysed against the share price time series using the Granger causality test to determine if one time series has predictive information about the share price time series over the same period of time. For this study, several Predictive Analytics (PA) techniques on tweets were evaluated to predict the Apple share price. To collect and analyse the data, a framework has been developed based on the LingPipe (LingPipe 2015) Natural Language Processing (NLP) tool kit for sentiment analysis, and using R, the functional language and environment for statistical computing, for correlation analysis. Twitter provides an API (Application Programming Interface) to access and collect its data programmatically. Whereas no clear correlation could be determined, at least one dataset was showed to have some predictive information on the development of the Apple share price. The other datasets did not show to have any predictive capabilities. There are many data analysis and PA techniques. The techniques applied in this study did not indicate a direct correlation. However, some results suggest that this is due to noise or asymmetric distributions in the datasets. The study contributes to the literature by providing a quantitative analysis of SM data, for example tweets about Apple and its most popular products, the iPad and iPhone. It shows how SM data can be used for PA. It contributes to the literature on Big Data and SMM by showing how SM data can be collected, analysed and classified and explore if the share price of a company can be determined based on sentiment time series. It may ultimately lead to better decision making, for instance for investments or share buyback

Caraterização de utilizadores em redes sociais

Mestrado em Engenharia de Computadores e TelemáticaO crescimento acentuado das Redes Sociais que se verificou num passado recente, criou uma nova área de estudo na investigação em análise e extração de dados. A sua disseminação pela sociedade moderna torna-as uma fonte interessante para a aplicação de ciência dos dados, visto que auxiliam a perceção de comportamentos e padrões em dados sociais. Este tipo de informação possui valor estratégico em áreas como a publicidade e o marketing. Nesta dissertação é apresentado um protótipo para uma aplicação web que visa apresentar informação sobre a rede Twitter e os utilizadores que a compõem, através de esquemas de visualização de dados. Esta aplicação adota um modelo de dados de um grafo de propriedades, armazenado numa base de dados de grafos, para permitir uma análise eficiente das relações entre os dados existentes no Twitter. Para além disso, também faz uso de algoritmos de aprendizagem supervisionados e não-supervisionados, assim como análise estatística, para extrair padrões no conteúdo de tweets e prever atributos latentes em utilizadores do Twitter. O objetivo final é permitir a caraterização dos utilizadores Portugueses do Twitter, através da interpretação dos resultados apresentados.The massive growth of Social Media platforms in recent years has created a new area of study for Data Mining research. Its general dissemination in modern society makes it a very interesting data science resource, as it enables the better understanding of social behavior and demographic statistics, information that has strategic value in business areas like marketing and advertising. This dissertation presents a prototype for a web application that provides a number of intuitive and interactive data visualization schemes that present information about the Twitter network and its individual users. This application leverages a property graph data model, modeled from a collection of millions of tweets from the Portuguese community and stored in a state of the art graph database, to enable an efficient analysis of the existent relationships in Twitter data. It also makes use of Supervised and Unsupervised learning algorithms, as well as statistical analysis, to extract meaningful patterns in tweets content and predict latent attributes in Twitter users. The end goal is to allow the characterization of the Portuguese users in Twitter, through the created visual representations of the achieved results

Evolution of Privacy Loss in Wikipedia

The cumulative effect of collective online participation has an important and adverse impact on individual privacy. As an online system evolves over time, new digital traces of individual behavior may uncover previously hidden statistical links between an individual's past actions and her private traits. To quantify this effect, we analyze the evolution of individual privacy loss by studying the edit history of Wikipedia over 13 years, including more than 117,523 different users performing 188,805,088 edits. We trace each Wikipedia's contributor using apparently harmless features, such as the number of edits performed on predefined broad categories in a given time period (e.g. Mathematics, Culture or Nature). We show that even at this unspecific level of behavior description, it is possible to use off-the-shelf machine learning algorithms to uncover usually undisclosed personal traits, such as gender, religion or education. We provide empirical evidence that the prediction accuracy for almost all private traits consistently improves over time. Surprisingly, the prediction performance for users who stopped editing after a given time still improves. The activities performed by new users seem to have contributed more to this effect than additional activities from existing (but still active) users. Insights from this work should help users, system designers, and policy makers understand and make long-term design choices in online content creation systems

Microcelebrity Practices: A Cross-Platform Study Through a Richness Framework

Social media have introduced a contemporary shift from broadcast to participatory media, which has brought about major changes to the celebrity management model. It is now common for celebrities to bypass traditional mass media and take control over their promotional discourse through the practice of microcelebrity. The theory of microcelebrity explains how people turn their public persona into media content with the goal of gaining and maintaining audiences who are regarded as an aggregated fan base. To accomplish this, the theory suggests that people employ a set of online self-presentation techniques that typically consist of three core practices: identity constructions, fan interactions and promoting visibility beyond the existing fan base. Studies on single platforms (e.g., Twitter), however, show that not all celebrities necessarily engage in all core practices to the same degree. Importantly, celebrities are increasingly using multiple social media platforms simultaneously to expand their audience, while overcoming the limitations of a particular platform. This points to a gap in the literature and calls for a cross-platform study. This dissertation employed a mixed-methods research design to reveal how social media platforms i.e., Twitter and Instagram, helped celebrities grow and maintain their audience. The first phase of the study relied on a richness scoring framework that quantified social media activities using affordance richness, a measure of the ability of a post to deliver the information necessary in affording a celebrity to perform an action by using social media artifacts. The analyses addressed several research questions regarding social media uses by different groups of celebrities and how the audience responded to different microcelebrity strategies. The findings informed the design of the follow-up interviews with audience members. Understanding expectations and behaviors of fans is relevant not only as a means to enhance the practice’s outcome and sustain promotional activity, but also as a contribution to our understandings about contemporary celebrity-fans relationships mediated by social media. Three findings are highlighted. First, I found that celebrities used the two platforms differently, and that different groups of celebrities emphasized different core practices. This finding was well explained by the interviews suggesting that the audiences had different expectations from different groups of celebrities. Second, microcelebrity strategies played an important role in an audience’s engagement decisions. The finding was supported by the interviews indicating that audience preferences were based on some core practices. Lastly, while their strategies had no effect on follow and unfollow decisions, the consistency of the practices had significant effects on the decisions. This study makes contributions to the theory of Microcelebrity and offers practical contributions by providing broad insights from both practitioners’ and audiences’ perspectives. This is essential given that microcelebrity is a learned practice rather than an inborn trait

Conceptualizing the Electronic Word-of-Mouth Process: What We Know and Need to Know About eWOM Creation, Exposure, and Evaluation

Electronic word of mouth (eWOM) is a prevalent consumer practice that has undeniable effects on the company bottom line, yet it remains an over-labeled and under-theorized concept. Thus, marketers could benefit from a practical, science-based roadmap to maximize its business value. Building on the consumer motivation–opportunity–ability framework, this study conceptualizes three distinct stages in the eWOM process: eWOM creation, eWOM exposure, and eWOM evaluation. For each stage, we adopt a dual lens—from the perspective of the consumer (who sends and receives eWOM) and that of the marketer (who amplifies and manages eWOM for business results)—to synthesize key research insights and propose a research agenda based on a multidisciplinary systematic review of 1050 academic publications on eWOM published between 1996 and 2019. We conclude with a discussion of the future of eWOM research and practice

Public Health 2.0: How Web 2.0 Sites Are Used by Patients with Type 2 Diabetes

Objective: Given the dramatic increase of new interactive features on the Internet known as Web 2.0 sites, the objective of this study was to determine how features such as member profiles, personal blogs and online social networks were used in virtual communities related to type 2 diabetes and to describe the potential differences between the social ecology model of these virtual communities and traditional physical communities.Methods: All original posts and replies in two diabetes discussion forums in web 2.0 enabled virtual communities were recorded for ninety days. Utilization of these features and content from publicly available components of profile pages were recorded from a purposive sample of 60 members. Content was analyzed using qualitative coding techniques. Utilization of other Web 2.0 features was recorded to determine frequency of use among sampled members.Results: 272 original posts and 3605 replies were generated by the participants in the discussion threads. Discussion forum analysis revealed that food, medication and blood glucose levels were major themes for original posts. Replies usually included the empathic and personal experiences of other members. Group guidance emerged from the cumulative responses provided by the community and provided the individual with a sense of the normalized behaviors of the community. Analysis of the utilization of various Web 2.0 features revealed that those who withheld gender information used the features less often than those identifying with a gender. Utilization also appeared to be dependent on the design attributes of the website. Analysis of 204 personal blog entries revealed the daily struggles of the members and rarely discussed diabetes. Replies to personal blogs were more likely to include religious guidance and expressions of empathy and love. Strong social ties were evident between individual blog entries and those providing the replies.Discussion and Public Health Significance: Discussion forums provided members with the ability to gather disease specific information from a large network of individuals with salient experiences. Personal blogs and other features facilitated the formation of strong social ties to develop. The combination of these features online provides a unique opportunity for public health practitioners to develop comprehensive and multifaceted interventions

Keeping Social Media Influencers Influential: Preserving Perceptions of Authenticity While Brand Dropping

Marketers’ use of social media influencers (SMIs)—individuals who use various social media channels to discuss a particular topic (e.g., fashion, health) or offer entertainment (e.g., comedy) and, in doing so, attract followers—to promote products, known as “influencer marketing,” is a widely employed and effective strategic tool (Linqia 2018). In fact, SMIs, who can be conceptualized as human brands (Thompson 2006), have a greater audience reach and dialogue generation compared to that of celebrities (Crimson Hexagon 2015). Further, consumers perceive SMIs’ content as trustworthy (Scott 2015), which is likely due to them being perceived as highly authentic. According to Audrezet, de Kerviler and Moulard (2018) SMIs use strategies to remain passionately authentic and transparently authentic. Despite their popularity and perceived trustworthiness, SMIs face a challenge when they mention, recommend, or endorse brands within their digital content. Doing so may lead to perceptions that the influencer is passionately inauthentic, as consumers may presume these acts to be commercially driven. Thus, by incorporating influencer marketing, SMIs may compromise their perceived passionate authenticity. When SMIs mention brands within their digital content, they sometimes choose to infer whether or not they have a business relationship with the brand via a disclosure. SMIs’ means of, or choice of wording for disclosures varies. Therefore, consumers will likely perceive SMIs as more transparently authentic when SMIs disclose unambiguously, since doing so implies complete forthrightness. SMIs are now required to disclose, or explicitly mention when they were paid to promote a brand (Johnson 2017). However, the FTC’s rules are somewhat ambiguous and perhaps unfair. Therefore, SMIs may or may not be explicitly disclosing their true relationship with brands they post about due to the sheer uncertainty and/or unfairness inherent in the FTC’s endorsement guidance (FTC 2015). SMIs who explicitly disclose are presumably perceived as possessing high transparent authenticity; however, such explicit disclosures presumably result in consumer perceptions of low passionate authenticity. This brings about a challenge to SMIs who partner with brands. This dissertation will answer the following question: How can social media influencers manage consumers’ perceptions of their human brand authenticity while engaging in influencer marketing