Uncovering Gender Bias within Journalist-Politician Interaction in Indian Twitter

Gender bias in political discourse is a significant problem on today's social media. Previous studies found that the gender of politicians indeed influences the content directed towards them by the general public. However, these works are particularly focused on the global north, which represents individualistic culture. Furthermore, they did not address whether there is gender bias even within the interaction between popular journalists and politicians in the global south. These understudied journalist-politician interactions are important (more so in collectivistic cultures like the global south) as they can significantly affect public sentiment and help set gender-biased social norms. In this work, using large-scale data from Indian Twitter we address this research gap. We curated a gender-balanced set of 100 most-followed Indian journalists on Twitter and 100 most-followed politicians. Then we collected 21,188 unique tweets posted by these journalists that mentioned these politicians. Our analysis revealed that there is a significant gender bias -- the frequency with which journalists mention male politicians vs. how frequently they mention female politicians is statistically significantly different ($p<<0.05$). In fact, median tweets from female journalists mentioning female politicians received ten times fewer likes than median tweets from female journalists mentioning male politicians. However, when we analyzed tweet content, our emotion score analysis and topic modeling analysis did not reveal any significant gender-based difference within the journalists' tweets towards politicians. Finally, we found a potential reason for the significant gender bias: the number of popular male Indian politicians is almost twice as large as the number of popular female Indian politicians, which might have resulted in the observed bias. We conclude by discussing the implications of this work

Understanding & controlling user privacy in social media via exposure

The recent popularity of Online Social Media sites (OSM) like Facebook and Twitter have led to a renewed discussion about user privacy. In fact, numerous recent news reports and research studies on user privacy stress the OSM users’ urgent need for better privacy control mechanisms. Thus, today, a key research question is: how do we provide improved privacy protection to OSM users for their social content? In this thesis, we propose a systematic approach to address this question. We start with the access control model, the dominant privacy model in OSMs today. We show that, while useful, the access control model does not capture many theoretical and practical aspects of privacy. Thus, we propose a new model, which we term the exposure control model. We define exposure for a piece of content as the set of people who actually view the content. We demonstrate that our model is a significant improvement over access control to capture users’ privacy requirements. Next, we investigate the effectiveness of our model to protect users’ privacy in three real world scenarios: (1) Understanding and controlling exposure using social access control lists (SACLs) (2) Controlling exposure by limiting large-scale social data aggregators and (3) Understanding and controlling longitudinal exposure in OSMs, i.e., how users control exposure of their old OSM content. We show that, in each of these cases, the exposure control-based approach helps us to design improved privacy control mechanisms.Die Popularität von sozialen Netzwerken (SN), wie Facebook, haben zu einer erneuten Diskussion über die Privatsphäre geführt. Wissenschaftliche Publikationen untersuchen die Privatsphäre und zeigen wie dringend SN Benutzer besseren Datenschutz benoötigen. Eine zentrale Herausforderung für in diesem Bereich ist: Wie kann der Schutz der Privatsphäre von SN Benutzern und ihren Inhalten garantiert werden? Diese Doktorarbeit schlägt Ansätze vor, die diese Frage beantworten. Wir untersuchen das Privatsphäremodel, das Access Control Modell, in SN. Wir zeigen auf, dass das Access Control Modell theoretische und praktische Aspekte der Privatsphäre nicht erfasst. Deshalb schlagen wir das Expositionssteuerunsgmodell vor und definieren Exposition für einen Inhalt als die Menge der Personen, die einen Beitrag ansieht. Unser Modell stellt eine bedeutende Verbesserung zu dem Access Control Modell dar. Wir untersuchen die Effektivität unseres Modells, indem wir den Datenschutz der Benutzer in drei realen Szenarien schützen: (1) Verständnis und Steuerung der Exposition von Inhalten mit Sozialen Access Control Listen (SACLs), (2) Steuerung der Exposition durch Begrenzung der umfassenden sozialen Datenaggregation und (3) Verständnis und Steuerung von Langzeitexposition in SN, z.B. wie Benutzer Exposition alter Inhalte begrenzen. In diesen Fällen fürt Expositionssteuerungsmethoden zu einem verbesserten Privatsphäresteuerungsmechanismus

Analyzing the Targets of Hate in Online Social Media

Social media systems allow Internet users a congenial platform to freely express their thoughts and opinions. Although this property represents incredible and unique communication opportunities, it also brings along important challenges. Online hate speech is an archetypal example of such challenges. Despite its magnitude and scale, there is a significant gap in understanding the nature of hate speech on social media. In this paper, we provide the first of a kind systematic large scale measurement study of the main targets of hate speech in online social media. To do that, we gather traces from two social media systems: Whisper and Twitter. We then develop and validate a methodology to identify hate speech on both these systems. Our results identify online hate speech forms and offer a broader understanding of the phenomenon, providing directions for prevention and detection approaches.Comment: Short paper, 4 pages, 4 table

Understanding and Specifying Social Access Control Lists

Online social network (OSN) users upload millions of pieces of contenttoshare with otherseveryday. While asignificant portionofthiscontentis benign(andistypicallysharedwith all friends or all OSN users), there are certain pieces of content that are highly privacy sensitive. Sharing such sensitive content raises significant privacy concerns for users, and it becomes important for the user to protect this content from being exposed to the wrong audience. Today, most OSN services provide fine-grained mechanisms for specifying social access control lists (social ACLs, or SACLs), allowing users to restrict their sensitive content to a select subset of their friends. However, it remains unclear how these SACL mechanisms are used today. To design better privacy management tools for users, we need to first understand the usage and complexity of SACLs specified by users. In this paper, we present the first large-scale study of finegrained privacy preferences of over 1,000 users on Facebook, providing us with the first ground-truth information on how users specify SACLs on a social networking service. Overall, we find that a surprisingly large fraction (17.6%) of content is shared with SACLs. However, we also find that the SACL membership shows little correlation with either profile information or social network links; as a result, it is difficult to predict the subset of a user’s friends likely to appear in a SACL. On the flip side, we find that SACLs are often reused, suggesting that simply making recent SACLs available to users is likely tosignificantly reduce the burdenof privacy management on users. 1

Impact of the pulse modulation format on distributed BOTDA sensors based on Simplex coding

We experimentally analyse the impact of pulse modulation format on BOTDA sensors exploiting Simplex coding. A careful optimisation of modulation format is required to avoid spurious oscillations causing severe penalties in the measurement accuracy

Uncovering Impact of Mental Models towards Adoption of Multi-device Crypto-Wallets

The ever-increasing cohort of cryptocurrency users saw a sharp increase in different types of crypto-wallets in the past decade. However, different wallets are non-uniformly adopted in the population today; Specifically, emerging multi-device wallets, even with improved security and availability guarantees over their counterparts, are yet to receive proportionate attention and adoption. This work presents a data-driven investigation into the perceptions of cryptocurrency users towards multi-device wallets today, using a survey of255crypto-wallet users. Our results revealed two significant groups within our participants—Newbies and Non-newbies. These two groups statistically significantly differ in their usage of crypto-wallets. However, both of these groups were concerned with the possibility of their keys getting compromised and yet are unfamiliar with the guarantees offered by multi-device wallets. After educating the participants about the more secure multi-device wallets, around 70% of the participants preferred them; However, almost one-third of participants were still not comfortable using them. Our qualitative analysis revealed a gap between the actual security guarantees and mental models for these participants—they were afraid that using multi-device wallets will result in losing control over keys (and in effect funds) due to the distribution of key shares. We also investigated the preferred default settings for crypto-wallets across our participants, since multi-device wallets allow a wide range of key-share distribution settings. In the distributed server settings of the multi-device wallets, the participants preferred a smaller number of reputed servers (as opposed to a large non-reputed pool). Moreover, considerations about the threat model further affected their preferences, signifying a need for contextualizing default settings. We conclude the discussion by identifying concrete, actionable design avenues for future multi-device wallet developers to improve adoption

SoK: Web3 Recovery Mechanisms

Account recovery enables users to regain access to their accounts when they lose their authentication credentials. While account recovery is well established and extensively studied in the Web2 (traditional web) context, Web3 account recovery presents unique challenges. In Web3, accounts rely on a (cryptographically secure) private-public key pair as their credential, which is not expected to be shared with a single entity like a server owing to security concerns. This makes account recovery in the Web3 world distinct from the Web2 landscape, often proving to be challenging or even impossible. As account recovery has proven crucial for Web2 authenticated systems, various solutions have emerged to address account recovery in the Web3 blockchain ecosystem in order to make it more friendly and accessible to everyday users, without punishing users if they make honest mistakes. This study systematically examines existing account recovery solutions within the blockchain realm, delving into their workflows, underlying cryptographic mechanisms, and distinct characteristics. After highlighting the trilemma between usability, security, and availability encountered in the Web3 recovery setting, we systematize the existing recovery mechanisms across several axes which showcase those tradeoffs. Based on our findings, we provide a number of insights and future research directions in this field