SIG SM Digest March 2022
Publications (Conference/Journal Papers)
We are excited to share the latest publications from the SIG SM community for the SIG SM community.
Thank you Vivek Singh, Laurie Bonnici, Amir Karami and Tara Zimmerman for sharing your research.
Authors: Isha Ghosh & Vivek Singh
Rutgers School of Communication and Information
Title: “Not all my friends are friends”: Audience-group-based nudges for managing location privacy
The popularity of location-based features in social networks has been increasing over the past few years. Location information gathered from social networks can threaten users' information privacy through granular tracking and exposure of their preferences, behaviors, and identity. In this 6-week study (N = 35), we investigate the effect of “audience-group”-based interventions on Facebook check-in behavior of participants. These “audience-group”-based nudges help close the gap between the users' perceivedaudiences and those that are permitted to view their check-ins. The nudges remind users that their real-time location information may be visible to a larger group of friends than they expect. Based on both quantitative and qualitative data analyses, we report that reminding users of the unexpected audiences that have access to their location check-ins could be a promising way to help users manage their privacy in online location sharing. These findings motivate several recommendations for app designers as well as information privacy researchers to better design and evaluate location sharing in online social networks.
Journal: Journal of the Association for Information Science and Technology
Bonnici, L., & Ma, J. (2021). What are they saying? A speech act analysis of a vaccination information debate on Facebook. Canadian Journal of Information & Library Science, 44(1), 19. DOI: https://doi.org/10.5206/cjilsrcsib.v44i1.13342
The global resurgence of vaccine preventable diseases is garnering attention amid the COVID-19 pandemic. Vaccination information debates in a Facebook group give participants access to second-hand knowledge conveying personal experiences. Through the lens of Speech Act Theory, this study analysed discourses on pro-and anti-vaccination perspectives along with views from vaccine hesitant groups. Analysis reveals significant criticism of behaviour around information. Findings indicate provision of substantiating information would play a crucial role in debate within divergent information contexts. Application of Speech Act Theory serves to inform participant communication more intimately and empowers their engagement in polarized discussion.
Karami, A., Lundy, M., Webb, F., Boyajieff, H. R., Zhu, M., & Lee, D. (2021). Automatic Categorization of LGBT User Profiles on Twitter with Machine Learning. Electronics, 10(15), 1822. https://doi.org/10.3390/electronics10151822
Privacy needs and stigma pose significant barriers to lesbian, gay, bisexual, and transgender (LGBT) people sharing information related to their identities in traditional settings and research methods such as surveys and interviews. Fortunately, social media facilitates people’s belonging to and exchanging information within online LGBT communities. Compared to heterosexual respondents, LGBT users are also more likely to have accounts on social media websites and access social media daily. However, the current relevant LGBT studies on social media are not efficient or assume that any accounts that utilize LGBT-related words in their profile belong to individuals who identify as LGBT. Our human coding of over 16,000 accounts instead proposes the following three categories of LGBT Twitter users: individual, sexual worker/porn, and organization. This research develops a machine learning classifier based on the profile and bio features of these Twitter accounts. To have an efficient and effective process, we use a feature selection method to reduce the number of features and improve the classifier’s performance. Our approach achieves a promising result with around 88% accuracy. We also develop statistical analyses to compare the three categories based on the average weight of top features.
Karami, A., Zhu, M., Goldschmidt, B., Boyajieff, H. R., & Najafabadi, M. M. (2021). COVID-19 Vaccine and Social Media in the US: Exploring Emotions and Discussions on Twitter. Vaccines, 9(10), 1059. https://doi.org/10.3390/vaccines9101059
The understanding of the public response to COVID-19 vaccines is the key success factor to control the COVID-19 pandemic. To understand the public response, there is a need to explore public opinion. Traditional surveys are expensive and time-consuming, address limited health topics, and obtain small-scale data. Twitter can provide a great opportunity to understand public opinion regarding COVID-19 vaccines. The current study proposes an approach using computational and human coding methods to collect and analyze a large number of tweets to provide a wider perspective on the COVID-19 vaccine. This study identifies the sentiment of tweets using a machine learning rule-based approach, discovers major topics, explores temporal trend and compares topics of negative and non-negative tweets using statistical tests, and discloses top topics of tweets having negative and non-negative sentiment. Our findings show that the negative sentiment regarding the COVID-19 vaccine had a decreasing trend between November 2020 and February 2021. We found Twitter users have discussed a wide range of topics from vaccination sites to the 2020 U.S. election between November 2020 and February 2021. The findings show that there was a significant difference between tweets having negative and non-negative sentiment regarding the weight of most topics. Our results also indicate that the negative and non-negative tweets had different topic priorities and focuses. This research illustrates that Twitter data can be used to explore public opinion regarding the COVID-19 vaccine.
Zimmerman, T., Njeri, M., Khader, M. and Allen, J. (2022), "Default to truth in information behavior: a proposed framework for understanding vulnerability to deceptive information", Information and Learning Sciences, Vol. 123 No. 1/2, pp. 111-126. Doi: 10.1108/ILS-08-2021-0067
Purpose – This study aims to recognize the challenge of identifying deceptive information and provides a framework for thinking about how we as humans negotiate the current media environment filled with misinformation and disinformation.
Design/methodology/approach – This study reviews the influence of Wilson’s (2016) General Theory of Information Behavior (IB) in the field of information science (IS) before introducing Levine’s Truth-Default Theory (TDT) as a method of deception detection. By aligning Levine’s findings with published scholarship on IB, this study illustrates the fundamental similarities between TDT and existing research in IS.
Findings – This study introduces a modification of Wilson’s work which incorporates truth-default, translating terms to apply this theory to the broader area of IB rather than Levine’s original face-to-face deception detection.
Originality/value – False information, particularly online, continues to be an increasing problem for both individuals and society, yet existing IB models cannot not account for the necessary step of determining the truth or falsehood of consumed information. It is critical to integrate this crucial decision point in this study’s IB models (e.g. Wilson’s model) to acknowledge the human tendency to default to truth and thus providing a basis for studying the twin phenomena of misinformation and disinformation from an IS perspective. Moreover, this updated model for IB contributes the Truth Default Framework for studying how people approach the daunting task of determining truth, reliability and validity in the immense number of news items, social media posts and other sources of information they encounter daily. By understanding and recognizing our human default to truth/trust, we can start to understand more about our vulnerability to misinformation and disinformation and be more prepared to guard against it.
Zimmerman, T. (2022). Social noise: The influence of observers on social media information behavior. Journal of Documentation. https://doi.org/10.1108/JD-08-2021-0165
Purpose: The purpose of this paper is to introduce the concept of social noise. Under the influence of social noise, a social media user may adjust information behavior based on external cues, attempting to present themselves in a more desirable way to increase their social capital.
Design/methodology/approach: A qualitative study informed by an ethnographic approach was used to examine social media information behavior. Participants were observed using Facebook, followed by semi-structured interviews. Data analysis was theoretically grounded in thematic analysis but also adaptive to observations in the data.
Findings: Four constructs of social noise were identified in the data. Identity curation emerged as the overarching consideration for individuals. The constructs cultural commitments and relationship management both had a strong presence within the data as well. The fourth construct, conflict management, was identified as social media users decided how to respond to individuals or information with which they did not agree.
Originality/value: This study reveals that social media users' awareness of observation by others does impact their information behavior. Efforts to craft a personal reputation, build or maintain relationships, pursue important commitments and manage conflict all influence the observable information behavior of social media users. As a result, observable social media information behavior may not be an accurate reflection of an individual's true thoughts and beliefs.