Researching Visual Social Media Platforms

Dhiraj Murthy is an Associate Professor of Journalism and Sociology at the University of Texas at Austin. He founded and directs the Computational Media Lab there. Murthy’s research explores social media, computational social science, race/ethnicity, qualitative/mixed methods, and disasters. Dr. Murthy has edited 3 journal special issues and authored over 60 articles, book chapters, and papers. Murthy wrote the first scholarly book about Twitter (second edition published by Polity Press, 2018). He is currently funded by the National Science Foundation’s Civil, Mechanical and Manufacturing Innovation (CMMI) Division for pioneering work on using the social media networks of journalists for damage reconnaissance during Hurricane Florence. Dr. Murthy’s work also uniquely explores the potential role of social technologies in diversity and community inclusion.With the meteoric rise of Instagram, Snapchat and YouTube, it is clear that image- and video- based platforms have become tremendously important to our social, political, and economic lives. However, there are unique challenges associated with data collection and analysis on visual social media platforms. This workshop explores the following questions in detail: How do we integrate and weigh Big Data questions with more in-depth contextualized analysis of social media content? How do we categorize textual and visual content, addressing issues of ontology? How can we scale small data to big data in visual spaces? Ultimately, it is argued that image/video data produced and consumed on social media has real value in helping us understand the social experience of everyday and profound events, but studying these types of data often requires innovations in theory and methods. Hands-on methods work will involve participants collecting data from YouTube and understanding structured metadata and unstructured data involving visual content

A South Asian American diasporic aesthetic community?

In the late 1990s, a diverse group of British South Asian musicians began to gain notoriety in the UK for their distinctive blends of synthesized beats with what were considered South Asian elements (e.g. tabla, sitar and `Hindustani' samples). Following these successes, the British media industries engaged in discourses on whether these South Asian musicians should be labelled under pre-existing musical genres such as acid jazz and electronic music or under an ethnically oriented classification such as `The Asian Underground'. Despite vociferous opposition, the latter categorization became the most promulgated. However, this discourse underwent a second iteration when South Asian musicians in New York City created a dance night largely influenced by their transatlantic diasporic colleagues. The purpose of this study was to examine the tensions between ethnically categorizing this New York dance night and not doing so. Using ethnographic data gathered during three months of fieldwork in 2001 as well as through a web-based questionnaire, this study yields interesting findings regarding not only ethnic labelling, but also the larger debate of ethnic essentialism. More specifically, the findings suggest that, on the one hand, ethnically labelling this dance hall as South Asian could facilitate an increased solidarity (sociopolitically) within the diaspora in New York City. While, on the other hand, such labelling could be dangerous to diasporic interests, as it essentializes the South Asian community into a homogenous entity

Predicting Gender and Political Affiliation Using Mobile Payment Data

We explore the understudied area of social payments to evaluate whether or not we can predict the gender and political affiliation of Venmo users based on the content of their Venmo transactions. Latent attribute detection has been successfully applied in the domain of studying social media. However, there remains a dearth of previous work using data other than Twitter. There is also a continued need for studies which explore mobile payments spaces like Venmo, which remain understudied due to the lack of data access. We hypothesize that using methods similar to latent attribute analysis with Twitter data, machine learning algorithms will be able to predict gender and political affiliation of Venmo users with a moderate degree of accuracy. We collected crowdsourced training data that correlates participants' political views with their public Venmo transaction history through the paid Prolific service. Additionally, we collected 21 million public Venmo transactions from recently active users to use for gender classification. We then ran the collected data through a TF-IDF vectorizer and used that to train a support vector machine (SVM). After hyperparameter training and additional feature engineering, we were able to predict user's gender with a high level of accuracy (.91) and had modest success predicting user's political orientation (.63).Comment: 10 pages, 5 figure

Visualizing Collective Discursive User Interactions in Online Life Science Communities

This paper highlights the rationale for the development of BioViz, a tool to help visualize the existence of collective user interactions in online life science communities. The first community studied has approximately 22,750 unique users and the second has 35,000. Making sense of the number of interactions between actors in these networks in order to discern patterns of collective organization and intelligent behavior is challenging. One of the complications is that forums - our object of interest - can vary in their purpose and remit (e.g. the role of gender in the life sciences to forums of praxis such as one exploring the cell line culturing) and this shapes the structure of the forum organization itself. Our approach took a random sample of 53 forums which were manually analyzed by our research team and interactions between actors were recorded as arcs between nodes. The paper focuses on a discussion of the utility of our approach, but presents some brief results to highlight the forms of knowledge that can be gained in identifying collective group formations. Specifically, we found that by using a matrix-based visualization approach, we were able to see patterns of collective behavior which we believe is valuable both to the study of collective intelligence and the design of virtual organizations.Comment: Presented at Collective Intelligence conference, 2012 (arXiv:1204.2991

PoxVerifi: An Information Verification System to Combat Monkeypox Misinformation

Following recent outbreaks, monkeypox-related misinformation continues to rapidly spread online. This negatively impacts response strategies and disproportionately harms LGBTQ+ communities in the short-term, and ultimately undermines the overall effectiveness of public health responses. In an attempt to combat monkeypox-related misinformation, we present PoxVerifi, an open-source, extensible tool that provides a comprehensive approach to assessing the accuracy of monkeypox related claims. Leveraging information from existing fact checking sources and published World Health Organization (WHO) information, we created an open-sourced corpus of 225 rated monkeypox claims. Additionally, we trained an open-sourced BERT-based machine learning model for specifically classifying monkeypox information, which achieved 96% cross-validation accuracy. PoxVerifi is a Google Chrome browser extension designed to empower users to navigate through monkeypox-related misinformation. Specifically, PoxVerifi provides users with a comprehensive toolkit to assess the veracity of headlines on any webpage across the Internet without having to visit an external site. Users can view an automated accuracy review from our trained machine learning model, a user-generated accuracy review based on community-member votes, and have the ability to see similar, vetted, claims. Besides PoxVerifi's comprehensive approach to claim-testing, our platform provides an efficient and accessible method to crowdsource accuracy ratings on monkeypox related-claims, which can be aggregated to create new labeled misinformation datasets.Comment: 11 pages, 5 figure

DisasterNet: Evaluating the Performance of Transfer Learning to Classify Hurricane-Related Images Posted on Twitter

Social media platforms are increasingly used during disasters. In the U.S., victims consider these platforms to be reliable news sources and they believe first responders will see what they publicly post. While having ways to request help during disasters might save lives, this information is difficult to find because non-relevant content on social media completely overshadows content reflective of who needs help. To resolve this issue, we develop a framework for classifying hurricane-related images that have been human-annotated. Our transfer learning framework classifies each image using the VGG-16 convolutional neural network and multi-layer perceptron classifiers according to the urgency, relevance, and time period, in addition to the presence of damage and relief motifs. We find that our framework not only successfully functions as an accurate method for hurricane-related image classification, but also that real-time classification of social media images using a small training set is possible

The potential for virtual communities to promote diversity in the sciences

This article investigates the role of online networks in providing support and mentoring resources for underrepresented groups. The case study of the virtual community of practice WomenScientists1 explores how online communities can be mobilized to help close the ‘leaky pipeline’ that too often leads women to leave the sciences after completing a post-secondary degree. The forum provides a virtual space for scientists around the world to discuss how gender impacts professional life in scientific fields, both within the academy and beyond. This project analyzes the content of WomenScientists1 to understand how users form mentoring relationships in the forum. Overcoming the underrepresentation of women in the sciences is a primary objective of international organizations including the European Union and UNESCO, both of which have made efforts to investigate how mentoring impacts long-term professional success. By examining textual data and the sentiment of posts, the article concludes that this virtual environment provides unique forms of support that specifically promote mentorship and the exchange of personalized advice for women in the life sciences

Moderating Social Media Discourse for a Healthy Democracy

The prevalence of hate speech and misinformation on the internet, heightened by the COVID-19 pandemic, directly harms minority groups that are the target of vitriol, as well as our society at large (Müller & Scwarz, 2020). In addition, the intersection between the two only exacerbates their harmful effects leading to an increase in intolerance and polarization (Kim & Kesari 2021). Current platform moderation techniques, as well as Section 230 under the Communications Decency Act, have been insufficient in addressing this problem, resulting in a lack of transparency from internet service providers, clear boundaries on user-platforms relations, and sufficient tools to handle a rapidly expanding internet. To address this problem space, we advocate for the following solutions: 1. Algorithmic governance & transparency: Internet Service Providers should be more transparent with users about content moderation policies and algorithms, and clarify users’ basic rights on the platform. 2. Flagging recommendations: We advocate a more effective, efficient and comprehensive flagging system through a combined strategy of content- and user-based approaches. 3. Multiplatform collaboration: Fighting harmful online content requires a collaborative effort among policy makers, civil society groups, researchers, and different platforms. 4. Long-term considerations: Building a regular and prolonged tracking system is essential to make anti-misinformation efforts more efficient and effective, especially in complex scenarios.Journalism and Medi

Automation, algorithms, and politics| bots and political influence: a sociotechnical investigation of social network capital

This study explains how bots interact with human users and influence conversational networks on Twitter. We analyze a high-stakes political environment, the UK general election of May 2015, asking human volunteers to tweet from purpose-made Twitter accounts—half of which had bots attached—during three events: the last Prime Minister’s Question Time before Parliament was dissolved (#PMQs), the first leadership interviews of the campaign (#BattleForNumber10), and the BBC Question Time broadcast of the same evening (#BBCQT). Based on previous work, our expectation was that our intervention would make a significant difference to the evolving network, but we found that the bots we used had very little effect on the conversation network at all. There are economic, social, and temporal factors that impact how a user of bots can influence political conversations. Future research needs to account for these forms of capital when assessing the impact of bots on political discussions