Abortion has been one of the most divisive issues in the United States. Yet, missing is comprehensive longitudinal evidence on how political divides on abortion are reflected in public discourse over time, on a national scale, and in response to key events before and after the overturn of Roe v Wade. We analyze a corpus of over 3.5M tweets related to abortion over the span of one year (January 2022 to January 2023) from over 1.1M users. We estimate users' ideology and rely on state-of-the-art transformer-based classifiers to identify expressions of hostility and extract five prominent frames surrounding abortion. We use those data to examine (a) how prevalent were expressions of hostility (i.e., anger, toxic speech, insults, obscenities, and hate speech), (b) what frames liberals and conservatives used to articulate their positions on abortion, and (c) the prevalence of hostile expressions in liberals and conservative discussions of these frames. We show that liberals and conservatives largely mirrored each other's use of hostile expressions: as liberals used more hostile rhetoric, so did conservatives, especially in response to key events. In addition, the two groups used distinct frames and discussed them in vastly distinct contexts, suggesting that liberals and conservatives have differing perspectives on abortion. Lastly, frames favored by one side provoked hostile reactions from the other: liberals use more hostile expressions when addressing religion, fetal personhood, and exceptions to abortion bans, whereas conservatives use more hostile language when addressing bodily autonomy and women's health. This signals disrespect and derogation, which may further preclude understanding and exacerbate polarization.

Affective polarization, the emotional divide between ideological groups marked by in-group love and out-group hate, has intensified in the United States, driving contentious issues like masking and lockdowns during the COVID-19 pandemic. Despite its societal impact, existing models of opinion change fail to account for emotional dynamics nor offer methods to quantify affective polarization robustly and in real-time. In this paper, we introduce a discrete choice model that captures decision-making within affectively polarized social networks and propose a statistical inference method estimate key parameters---in-group love and out-group hate---from social media data. Through empirical validation from online discussions about the COVID-19 pandemic, we demonstrate that our approach accurately captures real-world polarization dynamics and explains the rapid emergence of a partisan gap in attitudes towards masking and lockdowns. This framework allows for tracking affective polarization across contentious issues has broad implications for fostering constructive online dialogues in digital spaces.

The rich and dynamic information environment of social media provides researchers, policymakers, and entrepreneurs with opportunities to learn about social phenomena in a timely manner. However, using these data to understand social behavior is difficult due to the heterogeneity of topics and events discussed in the highly dynamic online information environment. To address these challenges, we present a method for systematically detecting and measuring emotional reactions to offline events using change point detection on the time series of collective affect and further explaining these reactions using a transformer-based topic model. We demonstrate the utility of the method by successfully detecting major and smaller events on three different datasets, including (1) a Los Angeles Tweet dataset between Jan. and Aug. 2020, in which we revealed the complex psychological impact of the BlackLivesMatter movement and the COVID-19 pandemic, (2) a dataset related to abortion rights discussions in the USA, in which we uncovered the strong emotional reactions to the overturn of Roe v. Wade and state abortion bans, and (3) a dataset about the 2022 French presidential election, in which we discovered the emotional and moral shift from positive before voting to fear and criticism after voting. We further demonstrate the importance of disaggregating data by topics and populations to mitigate potential biases when studying collective emotions. The capability of our method allows for better sensing and monitoring of the population’s reactions during crises using online data.

Online manipulation is a pressing concern for democracies, but the actions and strategies of coordinated inauthentic accounts, which have been used to interfere in elections, are not well understood. We analyze a five million-tweet multilingual dataset related to the 2017 French presidential election, when a major information campaign led by Russia called "#MacronLeaks" took place. We utilize heuristics to identify coordinated inauthentic accounts and detect attitudes, concerns and emotions within their tweets, collectively known as socio-linguistic characteristics. We find that coordinated accounts retweet other coordinated accounts far more than expected by chance, while being exceptionally active just before the second round of voting. Concurrently, socio-linguistic characteristics reveal that coordinated accounts share tweets promoting a candidate at three times the rate of non-coordinated accounts. Coordinated account tactics also varied in time to reflect news events and rounds of voting. Our analysis highlights the utility of socio-linguistic characteristics to inform researchers about tactics of coordinated accounts and how these may feed into online social manipulation.

The rich and dynamic information environment of social media provides researchers, policy makers, and entrepreneurs with opportunities to learn about social phenomena in a timely manner. However, using this data to understand social behavior is difficult due heterogeneity of topics and events discussed in the highly dynamic online information environment. To address these challenges, we present a method for systematically detecting and measuring emotional reactions to offline events using change point detection on the time series of collective affect, and further explaining these reactions using a transformer-based topic model. We demonstrate the utility of the method on a corpus of tweets from a large US metropolitan area between January and August, 2020, covering a period of great social change. We demonstrate that our method is able to disaggregate topics to measure population's emotional and moral reactions. This capability allows for better monitoring of population's reactions during crises using online data.

The growing prominence of social media in public discourse has led to a greater scrutiny of the quality of online information and the role it plays in amplifying political polarization. However, studies of polarization on social media platforms like Twitter have been hampered by the difficulty of collecting data about the social graph, specifically follow links that shape the echo chambers users join as well as what they see in their timelines. As a proxy of the follower graph, researchers use retweets, although it is not clear how this choice affects analysis. Using a sample of the Twitter follower graph and the tweets posted by users within it, we reconstruct the retweet graph and quantify its impact on the measures of echo chambers and exposure. While we find that echo chambers exist in both graphs, they are more pronounced in the retweet graph. We compare the information users see via their follower and retweet networks to show that retweeted accounts share systematically more polarized content. This bias cannot be explained by the activity or polarization within users' own follower graph neighborhoods but by the increased attention they pay to accounts that are ideologically aligned with their own views. Our results suggest that studies relying on the retweet graphs overestimate the echo chamber effects and exposure to polarized information.

Effective response to pandemics requires coordinated adoption of mitigation measures, like masking and quarantines, to curb a virus’s spread. However, as the COVID-19 pandemic demonstrated, political divisions can hinder consensus on the appropriate response. To better understand these divisions, our study examines a vast collection of COVID-19-related tweets. We focus on five contentious issues: coronavirus origins, lockdowns, masking, education, and vaccines. We describe a weakly supervised method to identify issue-relevant tweets and employ state-of-the-art computational methods to analyze moral language and infer political ideology. We explore how partisanship and moral language shape conversations about these issues. Our findings reveal ideological differences in issue salience and moral language used by different groups. We find that conservatives use more negatively-valenced moral language than liberals and that political elites use moral rhetoric to a greater extent than non-elites across most issues. Examining the evolution and moralization on divisive issues can provide valuable insights into the dynamics of COVID-19 discussions and assist policymakers in better understanding the emergence of ideological divisions.

On June 24, 2022, the United States Supreme Court overturned landmark rulings made in its 1973 verdict in Roe v. Wade. The justices by way of a majority vote in Dobbs v. Jackson Women's Health Organization, decided that abortion wasn't a constitutional right and returned the issue of abortion to the elected representatives. This decision triggered multiple protests and debates across the US, especially in the context of the midterm elections in November 2022. Given that many citizens use social media platforms to express their views and mobilize for collective action, and given that online debate provides tangible effects on public opinion, political participation, news media coverage, and the political decision-making, it is crucial to understand online discussions surrounding this topic. Toward this end, we present the first large-scale Twitter dataset collected on the abortion rights debate in the United States. We present a set of 74M tweets systematically collected over the course of one year from January 1, 2022 to January 6, 2023.

Mobile devices are expected to be always connected, and this implies that the mobile network is able to quickly identify and address faults that impact service (for example, due to power outages). In this article, we present our approach for automatically detecting mass power outages. Our solution decreases the number of created trouble tickets in two mobile networks by 4.7% and 9.3%.

The Internet has been experiencing immense growth in multimedia traffic from mobile devices. The increase in traffic presents many challenges to user-centric networks, network operators, and service providers. Foremost among these challenges is the inability of networks to determine the types of encrypted traffic and thus the level of network service the traffic needs to maintain an acceptable quality of experience. Therefore, end devices are a natural fit for performing traffic classification since end devices have more contextual information about device usage and traffic. This paper proposes a novel approach that classifies multimedia traffic types produced and consumed on mobile devices. The technique relies on a mobile device’s detection of its multimedia context characterized by its utilization of different media input/output (I/O) components, e.g., camera, microphone, and speaker. We develop an algorithm, MediaSense, which senses the states of multiple I/O components and identifies the specific multimedia context of a mobile device in real-time. We demonstrate that MediaSense classifies encrypted multimedia traffic in real-time as accurately as deep learning approaches and with even better generalizability.

The transparency and privacy behavior of mobile browsers has remained widely unexplored by the research community. In fact, as opposed to regular Android apps, mobile browsers may present contradicting privacy behaviors. On the one end, they can have access to (and can expose) a unique combination of sensitive user data, from users’ browsing history to permission-protected personally identifiable information (PII) such as unique identifiers and geolocation. However, on the other end, they also are in a unique position to protect users’ privacy by limiting data sharing with other parties by implementing ad-blocking features. In this paper, we perform a comparative and empirical analysis on how hundreds of Android web browsers protect or expose user data during browsing sessions. To this end, we collect the largest dataset of Android browsers to date, from the Google Play Store and four Chinese app stores. Then, we developed a novel analysis pipeline that combines static and dynamic analysis methods to find a wide range of privacy-enhancing (e.g., ad-blocking) and privacy-harming behaviors (e.g., sending browsing histories to third parties, not validating TLS certificates, and exposing PII---including non-resettable identifiers---to third parties) across browsers. We find that various popular apps on both Google Play and Chinese stores have these privacy-harming behaviors, including apps that claim to be privacy-enhancing in their descriptions. Overall, our study not only provides new insights into important yet overlooked considerations for browsers’ adoption and transparency, but also that automatic app analysis systems (e.g., sandboxes) need context-specific analysis to reveal such privacy behaviors.

The Internet has been experiencing immense growth in multimedia traffic from mobile devices. The increase in traffic presents many challenges to user-centric networks, network operators, and service providers. Foremost among these challenges is the inability of networks to determine the types of encrypted traffic and thus the level of network service the traffic needs for maintaining an acceptable quality of experience. Therefore, end devices are a natural fit for performing traffic classification since end devices have more contextual information about the device usage and traffic. This paper proposes a novel approach that classifies multimedia traffic types produced and consumed on mobile devices. The technique relies on a mobile device’s detection of its multimedia context characterized by its utilization of different media input/output components, e.g., camera, microphone, and speaker. We develop an algorithm, MediaSense, which senses the states of multiple I/O components and identifies the specific multimedia context of a mobile device in real-time. We demonstrate that MediaSense classifies encrypted multimedia traffic in real-time as accurately as deep learning approaches and with even better generalizability.

Offering seamless connectivity to devices capable of simultaneously using multiple communication interfaces continues to be a hard problem. This problem is important for edge computing because edge services may be available only on a subset of networks to which the device is capable of connecting to. We argue that various aspects of this problem can be addressed by leveraging the current trends of using user space libraries for networking, and allowing control plane negotiations between user devices and networks.

We use various multimedia applications on smart devices to consume multimedia content, to communicate with our peers, and to broadcast our events live. This paper investigates the utilization of different media input/output devices, e.g., camera, microphone, and speaker, by different types of multimedia applications, and introduces the notion of multimedia context. Our measurements lead to a sensing algorithm called MediaSense, which senses the states of multiple I/O devices and identifies eleven multimedia contexts of a mobile device in real time. The algorithm distinguishes stored content playback from streaming, live broadcasting from local recording, and conversational multimedia sessions from GSM/VoLTE calls on mobile devices.

The virtual resources of 5G networks are expected to scale and support migration to other locations within the substrate. In this context, a configuration for 5G network slices details the instantaneous mapping of the virtual resources across all slices on the substrate, and a feasible configuration satisfies the Service-Level Objectives (SLOs) without overloading the substrate. Reconfiguring a network from a given source configuration to the desired target configuration involves identifying an ordered sequence of feasible configurations from the source to the target. The proposed solutions for finding such a sequence are optimized for data centers and cannot be used as-is for reconfiguring 5G network slices. We present Matryoshka, our divide-and-conquer approach for finding a sequence of feasible configurations that can be used to reconfigure 5G network slices. Unlike previous approaches, Matryoshka also considers the bandwidth and latency constraints between the network functions of network slices. Evaluating Matryoshka required a dataset of pairs of source and target configurations. Because such a dataset is currently unavailable, we analyze proof of concept roll-outs, trends in standardization bodies, and research sources to compile an input dataset. On using Matryoshka on our dataset, we observe that it yields close-to-optimal reconfiguration sequences 10X faster than existing approaches.

The response time of an online service depends on the tail latency of a few of the applications it invokes in parallel to satisfy the requests. The individual applications are composed of one or more threads to fully utilize the available CPU cores, but this approach can incur serious overheads. The thread-per-core architecture has emerged to reduce these overheads, but it also has its challenges from thread synchronization and OS interfaces. Applications can mitigate both issues with different techniques, but their impact on application tail latency is an open question. We measure the impact of thread-per-core architecture on application tail latency by implementing a key-value store that uses application-level partitioning, and inter-thread messaging and compare its tail latency to Memcached which uses a traditional key-value store design. We show in an experimental evaluation that our approach reduces tail latency by up to 71 % compared to baseline Memcached running on commodity hardware and Linux. However, we observe that the thread-per-core approach is held back by request steering and OS interfaces, and it could be further improved with NIC hardware offload.

I/O is getting faster in servers that have fast programmable NICs and non-volatile main memory operating close to the speed of DRAM, but single-threaded CPU speeds have stagnated. Applications cannot take advantage of modern hardware capabilities when using interfaces built around abstractions that assume I/O to be slow. We therefore propose a structure for an OS called parakernel, which eliminates most OS abstractions and provides interfaces for applications to leverage the full potential of the underlying hardware. The parakernel facilitates application-level parallelism by securely partitioning the resources and multiplexing only those resources that are not partitioned.

5G networks leverage network slices for serving use cases with different and potentially conflicting requirements. Current approaches for composing network slices largely overlook the presence of multiple ingress and egress nodes in each network slice. This results in inefficient resource usage even when co-locating the Virtual Network Functions (VNFs) of a slice. The network is thus quickly saturated, and no more use cases can be served. We address this issue by introducing μslices, where each μslice is tied to a specific pair of ingress and egress nodes of a slice. Then, we optimize the bandwidth consumed by each μslice by co-locating its VNF instances that communicate the most. We observed that enabling μslicing was able to save about two times more control and data plane traffic and it also resulted in a more even distribution of computational and link load.

Mobile networks are expected to serve a wide range of verticals, however the Long Term Evolution (LTE) network is optimized for basic mobile operator services only. The network functions serving LTE networks are largely implemented as dedicated single function devices that offer poor customization options. This intrinsic inflexibility makes current LTE networks unable to meet the requirements of future mobile networks. For example, LTE networks experience signaling storms because the signals exchanged by the network functions cannot be optimized according to the current usage pattern of mobile services. Modularizing these network functions would enable a refactoring of the LTE network, allowing operators to compose networks that adapt and evolve with the influx of verticals. In this article, we present a new approach for refactoring the network functions serving LTE networks which can be leveraged to compose a modular mobile network optimized for the verticals using its services. As an example, we demonstrate that deploying network functions at the edge significantly reduces the signals exchanged within a mobile network.

It is well known that apps running on mobile devices extensively track and leak users' personally identifiable information (PII); however, these users have little visibility into PII leaked through the network traffic generated by their devices, and have poor control over how, when and where that traffic is sent and handled by third parties. In this paper, we present the design, implementation, and evaluation of ReCon: a cross-platform system that reveals PII leaks and gives users control over them without requiring any special privileges or custom OSes. ReCon leverages machine learning to reveal potential PII leaks by inspecting network traffic, and provides a visualization tool to empower users with the ability to control these leaks via blocking or substitution of PII. We evaluate ReCon's effectiveness with measurements from controlled experiments using leaks from the 100 most popular iOS, Android, and Windows Phone apps, and via an IRB-approved user study with 92 participants. We show that ReCon is accurate, efficient, and identifies a wider range of PII than previous approaches.

Wi-Fi networks were one of the first use-cases for Software-defined networking (SDN). However, to deploy a software-defined Wi-Fi network today, one has to rely on research prototypes with availability, documentation, hardware requirements, and scalability issues. To alleviate this situation, we demonstrate two simple techniques to bring SDN functionality to existing Wi-Fi networks and discuss their benefits and short-comings. Researchers can use our techniques to convert their existing Wi-Fi testbeds into software defined Wi-Fi testbeds. Our two techniques thus significantly lower the barrier-to-entry for deploying software-defined Wi-Fi networks.

Online social networks (OSNs) are an important source of information for scientists in different fields such as computer science, sociology, economics, etc. However, it is hard to study OSNs as they are very large. For instance, Facebook has 1.28 billion active users in March 2014 and Twitter claims 255 million active users in April 2014. Also, companies take measures to prevent crawls of their OSNs and refrain from sharing their data with the research community. For these reasons, we argue that sampling techniques will be the best technique to study OSNs in the future. In this work, we take an experimental approach to study the characteristics of well-known sampling techniques on a full social graph of Twitter crawled in 2012 [2]. Our contribution is to evaluate the behavior of these techniques on a real directed graph by considering two sampling scenarios: (a) obtaining most popular users (b) obtaining an unbiased sample of users, and to find the most suitable sampling techniques for each scenario.

Twitter is one of the largest social networks using exclusively directed links among accounts. This makes the Twitter social graph much closer to the social graph supporting real life communications than, for instance, Facebook. Therefore, understanding the structure of the Twitter social graph is interesting not only for computer scientists, but also for researchers in other fields, such as sociologists. However, little is known about how the information propagation in Twitter is constrained by its inner structure. In this paper, we present an in-depth study of the macroscopic structure of the Twitter social graph unveiling the highways on which tweets propagate, the specific user activity associated with each component of this macroscopic structure, and the evolution of this macroscopic structure with time for the past 6 years. For this study, we crawled Twitter to retrieve all accounts and all social relationships (follow links) among accounts; the crawl completed in July 2012 with 505 million accounts interconnected by 23 billion links. Then, we present a methodology to unveil the macroscopic structure of the Twitter social graph. This macroscopic structure consists of 8 components defined by their connectivity characteristics. Each component group users with a specific usage of Twitter. For instance, we identified components gathering together spammers, or celebrities. Finally, we present a method to approximate the macroscopic structure of the Twitter social graph in the past, validate this method using old datasets, and discuss the evolution of the macroscopic structure of the Twitter social graph during the past 6 years.

In this paper, we address the trade-off between the data plane efficiency and the control plane timeliness for the BitTorrent performance. We argue that loss-based congestion control protocols can fill large buffers, leading to a higher end-to-end delay, unlike low-priority or delay-based congestion control protocols. We perform experiments for both the uTorrent and mainline BitTorrent clients, and we study the impact of uTP (a novel transport protocol proposed by BitTorrent) and several TCP congestion control algorithms (Cubic, New Reno, LP, Vegas and Nice) on the download completion time. Briefly, in case peers in the swarm all use the same congestion control algorithm, we observe that the specific algorithm has only a limited impact on the swarm performance. Conversely, when a mix of TCP congestion control algorithms coexists, peers employing a delay-based low-priority algorithm exhibit shorter completion time.

Video streaming represents a large fraction of Internet traffic. Surprisingly, little is known about the network characteristics of this traffic. In this paper, we study the network characteristics of the two most popular video streaming services, Netflix and YouTube. We show that the streaming strategies vary with the type of the application (Web browser or native mobile application), and the type of container (Silverlight, Flash, or HTML5) used for video streaming. In particular, we identify three different streaming strategies that produce traffic patterns from non-ack clocked ON-OFF cycles to bulk TCP transfer. We then present an analytical model to study the potential impact of these streaming strategies on the aggregate traffic and make recommendations accordingly.

Network latency and packet loss are considered to be an important requirement for realistic evaluation of Peer-to-Peer protocols. Dedicated clusters, such as Grid'5000, do not provide the variety of network latency and packet loss rates that can be found in the Internet. However, compared to the experiments performed on testbeds such as PlanetLab, the experiments performed on dedicated clusters are reproducible, as the computational resources are not shared. In this paper, we perform experiments to study the impact of network latency and packet loss on the time required to download a file using BitTorrent. In our experiments, we observe a less than 15% increase on the time required to download a file when we increase the round-trip time between any two peers, from 0 ms to 400 ms, and the packet loss rate, from 0% to 5%. Our main conclusion is that the underlying network latency and packet loss have a marginal impact on the time required to download a file using BitTorrent. Hence, dedicated clusters such as Grid'5000 can be safely used to perform realistic and reproducible BitTorrent experiments.

We model a segment of filtered speech signal as a product of elementary signals as opposed to a sum of sinusoidal signals. Using this model, one can better appreciate the basic relationships between envelopes and phases or instantaneous frequencies (IFs) of signals. These relationships reveal some interesting properties of the signal's modulations. For instance, if the contribution due to a signal's envelope, specifically the Hilbert transform of its log-envelope, is removed from the signal's phase, then the resulting signal's IF is strictly positive. In addition, filtered speech signal having a bandwidth of B Hz can be essentially represented by the log-envelope and IF that have the same B Hz bandwidths. We extend the above ideas to decompose speech into modulated components. Specifically, a bank of data-adaptive filters (in a cross-coupled configuration) are used to decompose speech into its components; each adaptive filter is a simple single resonance bandpass filter (whose center-frequency or pole-location closely follows the desired formant frequency) supplemented by an adaptive all-zero filter (whose zero-locations sufficiently reduce unwanted leakage from neighboring formants). The filtered components are then represented by their respective log-envelopes and positive IFs; these small number of modulations closely approximate the speech signal.

An analytic signal permits unambiguous characterization of the phase and envelope of a real signal. But the analytic signal's phase-derivative, i.e. the instantaneous frequency (IF) is typically a wild function and can take on values ranging from negative infinity to positive infinity. Fortunately, any analytic signal can be decomposed into a minimum phase (MinP) signal component and an all-phase (AllP) signal component. While the MinP signal's log-envelope and its phase form a Hilbert transform pair, the AllP signal has a positive definite instantaneous frequency (PIF) unlike that of the original analytic signal. We propose an elegant computational algorithm that separates the MinP and AllP components of the analytic signal. The envelope of the MinP component corresponds to the AM and the PIF of the AllP component corresponds to the positive FM.

A parametric approach to compute the Hilbert transform (HT) of a real signal is proposed. A scaled version of the real signal is raised to the exponent of Napier's base, and an all-pole signal model is fitted to approximate the resulting signal as though it were the envelope. An error criterion that flattens this envelope is then minimized over a set of complex Fourier coefficients. The resulting approximation is guaranteed to be a minimum-phase (MinP) signal. Since a MinP signal has the property that the logarithm of its envelope and its phase are related by the HT, it follows that the resulting phase of MinP approximation yields the desired HT.

In an earlier paper, Rao and Kumaresan (see Proc. of the 29-th Asilomar Conf. on Sig., Sys., and Comp., 1995) proposed a new algorithm, called the parametric constant modulus algorithm (PCMA) to demodulate noiseless, synthetic as well as real-world, cochannel FM signals. They extend the PCMA to develop two new procedures. The first one addresses the problem of reserving two closely spaced noisy sinewaves (from a short data record). The second algorithm is applied to estimate the phase parameters of two superimposed noisy chirp signals; both algorithm's performance are compared with the Cramer-Rao bounds. The techniques described are of interest in wireless/military communications in the context of carrier frequency and Doppler estimation.

A procedure to decompose a signal consisting of nonstationary sinusoidal components based on the principles of residual signal analysis is proposed. A tracking unit consisting of an all zero filter (AZF) in cascade with a dynamic tracking filter (DTF) is assigned to each component. While the adaptively varying zeros of the AZF suppresses all interfering neighbors, the DTF captures the slowly varying instantaneous frequency (IF) of the desired component. Our earlier methods improved upon Costas's estimator-predictor filter bank by using a better interfering signal predictor. The alternative procedure described in this paper avoids the use of prediction and is not overly restricted by the number of components. We also show that by using two simple feedback loops (a loop-filter is thus avoided) the tracking information is ensured to be in phase. Finally, the algorithms ability to decompose synthetic signals, speech signals into harmonic partials as well as tracking the formants present in voiced speech segments is illustrated.