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1. The Fog of Warnings: How Non-essential Notifications Blur with Security Warnings

   Vance, A. (August 2019, Symposium on Usable Privacy and Security (SOUPS))

   Adherence to security warnings continues to be an important problem in information security. Although users may fail to heed a security warning for a variety of reasons, a major contributor is habituation, which is decreased response to repeated stimulation. However, the scope of this problem may actually be much broader than previously thought because of the neurobiological phenomenon of generalization. Whereas habituation describes a diminished response with repetitions of the same stimulus, generalization occurs when habituation to one stimulus carries over to other novel stimuli that are similar in appearance. Generalization has important implications for the domains of usable security and human–computer interaction. Because a basic principle of user interface design is visual consistency, generalization suggests that through exposure to frequent non-security-related notifications (e.g., dialogs, alerts, confirmations, etc.) that share a similar look and feel, users may become deeply habituated to critical security warnings that they have never seen before. Further, with the increasing number of notifications in our lives across a range of mobile, Internet of Things, and computing devices, the accumulated effect of generalization may be substantial. However, this problem has not been empirically examined before. This paper contributes by measuring the impacts of generalization in terms of (1) diminished attention via mouse cursor tracking and (2) users’ ability to behaviorally adhere to security warnings. Through an online experiment, we find that: • Habituation to a frequent non-security-related notification does carry over to a one-time security warning. • Generalization of habituation is manifest both in (1) decreased attention to warnings and (2) lower warning adherence behavior. • The carry-over effect, most importantly, is due to generalization, and not fatigue. • The degree that generalization occurs depends on the similarity in look and feel between a notification and warning. These findings open new avenues of research and provide guidance to software developers for creating warnings that are more resistant to the effects of generalization of habituation, thereby improving users’ security warning adherence. 

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2. Warning users about cyber threats through sounds

   https://doi.org/10.1007/s42452-021-04703-4  

   Datta, Prerit; Namin, Akbar Siami; Jones, Keith S.; Hewett, Rattikorn (June 2021, SN Applied Sciences)

   AbstractThis paper reports a formative evaluation of auditory representations of cyber security threat indicators and cues, referred to as sonifications, to warn users about cyber threats. Most Internet browsers provide visual cues and textual warnings to help users identify when they are at risk. Although these alarming mechanisms are very effective in informing users, there are certain situations and circumstances where these alarming techniques are unsuccessful in drawing the user’s attention: (1) security warnings and features (e.g., blocking out malicious Websites) might overwhelm a typical Internet user and thus the users may overlook or ignore visual and textual warnings and, as a result, they might be targeted, (2) these visual cues are inaccessible to certain users such as those with visual impairments. This work is motivated by our previous work of the use of sonification of security warnings to users who are visually impaired. To investigate the usefulness of sonification in general security settings, this work uses real Websites instead of simulated Web applications with sighted participants. The study targets sonification for three different types of security threats: (1) phishing, (2) malware downloading, and (3) form filling. The results show that on average 58% of the participants were able to correctly remember what the sonification conveyed. Additionally, about 73% of the participants were able to correctly identify the threat that the sonification represented while performing tasks using real Websites. Furthermore, the paper introduces “CyberWarner”, a sonification sandbox that can be installed on the Google Chrome browser to enable auditory representations of certain security threats and cues that are designed based on several URL heuristics. Article highlightsIt is feasible to develop sonified cyber security threat indicators that users intuitively understand with minimal experience and training.Users are more cautious about malicious activities in general. However, when navigating real Websites, they are less informed. This might be due to the appearance of the navigating Websites or the overwhelming issues when performing tasks.Participants’ qualitative responses indicate that even when they did not remember what the sonification conveyed, the sonification was able to capture the user’s attention and take safe actions in response. 

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3. Paths to Empathy: Heterogeneous Effects of Reading Personal Stories Online

   Roshanaei, M.; Tran, C.; Morelli, S.; Caragea, C.; Zheleva, E. (January 2019, Proceedings of IEEE Conference on Data Science and Advanced Analytics (DSAA))

   Every day people share personal stories online, reaching millions of users around the world through blogs, social media and news websites. Why are some of these stories more attractive to readers than others? What features of these personal narratives make readers empathize with the storyteller? Do the readers’ personal characteristics and experiences play a role in feeling connection to the story they read? Experimental studies in psychology show that there are several factors that increase empathy in the aggregate, but there is a need for deeper understanding of empathetic feelings at the individual level of storyteller, story, and reader. Here, we present the design and analysis of a survey that studied the impact of story features and reader predispositions and perceptions on the empathy they feel when reading online stories. We use causal trees to find the individual-level causal factors for empathy and to understand the heterogeneity in the treatment effects. One of our main findings is that empathy is contextual and, while reader personality plays a significant role in evoking empathy, the mood of the reader prior to reading the story and linguistic story features have an impact as well. The results of our analyses can be used to help people create content that others care about and to help them communicate more effectively 

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4. Algorithmic nudge: Using XAI frameworks to design interventions

   Juneja, Prerna; Mitra, Tanushree (January 2021, ACM CHI Workshop on Operationalizing Human-Centered Perspectives in Explainable AI)

   In this position paper, we propose the use of existing XAI frameworks to design interventions in scenarios where algorithms expose users to problematic content (e.g. anti vaccine videos). Our intervention design includes facts (to indicate algorithmic justification of what happened) accompanied with either fore warnings or counterfactual explanations. While fore warnings indicate potential risks of an action to users, the counterfactual explanations will indicate what actions user should perform to change the algorithmic outcome. We envision the use of such interventions as `decision aids' to users which will help them make informed choices. 

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5. BANANA: when Behavior ANAlysis meets social Network Alignment

   https://doi.org/10.24963/ijcai.2020/200  

   Ren, Fuxin; Zhang, Zhongbao; Zhang, Jiawei; Su, Sen; Sun, Li; Zhu, Guozhen; Guo, Congying (July 2020, International Joint Conference on Artificial Intelligence)

   null (Ed.)

   Recently, aligning users among different social networks has received significant attention. However, most of the existing studies do not consider users’ behavior information during the aligning procedure and thus still suffer from the poor learning performance. In fact, we observe that social network alignment and behavior analysis can benefit from each other. Motivated by such an observation, we propose to jointly study the social network alignment problem and user behavior analysis problem. We design a novel end-to-end framework named BANANA. In this framework, to leverage behavior analysis for social network alignment at the distribution level, we design an earth mover’s distance based alignment model to fuse users’ behavior information for more comprehensive user representations. To further leverage social network alignment for behavior analysis, in turn, we design a temporal graph neural network model to fuse behavior information in different social networks based on the alignment result. Two models above can work together in an end-to-end manner. Through extensive experiments on real-world datasets, we demonstrate that our proposed approach outperforms the state-of-the-art methods in the social network alignment task and the user behavior analysis task, respectively. 

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