With the increasing popularity of online social media (e.g., Facebook, Twitter, Reddit), the detection of misleading content on social media has become a critical undertaking. This paper focuses on an important but largely unsolved problem: detecting fauxtography (i.e., social media posts with misleading images). We found that the existing literature falls short in solving this problem. In particular, current solutions either focus on the detection of fake images or misinformed texts of a social media post. However, they cannot solve our problem because the detection of fauxtography depends not only on the truthfulness of the images and the texts but also on the information they deliver together on the posts. In this paper, we develop the FauxBuster, an end-to-end supervised learning scheme that can effectively track down fauxtography by exploring the valuable clues from user's comments of a post on social media. The FauxBuster is content-free in that it does not rely on the analysis of the actual content of the images, and hence is robust against malicious uploaders who can intentionally modify the presentation and description of the images. We evaluate FauxBuster on real-world data collected from two mainstream social media platforms - Reddit and Twitter. Results show that our scheme is both effective and efficient in addressing the fauxtography problem.

This paper introduces a novel paradigm for resource management in distributed systems, called decision-driven execution. The paradigm is appropriate for mission-driven systems, where the goal is to enable faster, leaner, and more effective decision making. All resource consumption, in this paradigm, is tied to the needs of making decisions on alternative courses of action. A point of departure from traditional architectures lies in interfaces that allow applications to specify their underlying decision logic. This specification, in turn, allows the system to reason about most effective means to meet information needs of decisions, resulting in simultaneous optimization of decision accuracy, cost, and speed. The paper discusses the overall vision of decision-driven execution, outlining preliminary work and novel challenges.

Emerging distributed in-memory computing frameworks, such as Apache Spark, can process a huge amount of cached data within seconds. This remarkably high efficiency requires the system to well balance data across tasks and ensure data locality. However, it is challenging to satisfy these requirements for applications that operate on a collection of dynamically loaded and evicted datasets. The dynamics may lead to time-varying data volume and distribution, which would frequently invoke expensive data re-partition and transfer operations, resulting in high overhead and large delay. To address this problem, we present Stark, a system specifically designed for optimizing in-memory computing on dynamic dataset collections. Stark enforces data locality for transformations spanning multiple datasets (e.g., join and cogroup) to avoid unnecessary data replications and shuffles. Moreover, to accommodate fluctuating data volume and skeweddata distribution, Stark delivers elasticity into partitions to balance task execution time andreduce job makespan. Finally, Stark achieves bounded failure recovery latency byoptimizing the data checkpointing strategy. Evaluations on a 50-server cluster show that Stark reduces the job makespan by 4X and improves system throughput by 6X compared to Spark.

This paper addresses the problem of choosing the right sources to solicit data from in sensing applications involving broadcast channels, such as those crowdsensing applications where sources share their observations on social media. The goal is to select sources such that expected fusion error is minimized. We assume that soliciting data from a source incurs a cost and that the cost budget is limited. Contrary to other formulations of this problem, we focus on the case where some sources influence others. Hence, asking a source to make a claim affects the behavior of other sources as well, according to an influence model. The paper makes two contributions. First, we develop an analytic model for estimating expected fusion error, given a particular influence graph and solution to the source selection problem. Second, we use that model to search for a solution that minimizes expected fusion error, formulating it as a zero-one integer non-linear programming (INLP) problem. To scale the approach, the paper further proposes a novel reliability-based pruning heuristic (RPH) and a similarity-based lossy estimation (SLE) algorithm that significantly reduce the complexity of the INLP algorithm at the cost of a modest approximation. The analytically computed expected fusion error is validated using both simulations and real-world data from Twitter, demonstrating a good match between analytic predictions and empirical measurements. It is also shown that our method outperforms baselines in terms of resulting fusion error.

This paper presents unsupervised algorithms to uncover polarization in social networks (namely, Twitter) and identify polarized groups. The approach is language-agnostic and thus broadly applicable to global and multilingual media. In cases of conflict, dispute, or situations involving multiple parties with contrasting interests, opinions get divided into different camps. Previous manual inspection of tweets has shown that such situations produce distinguishable signatures on Twitter, as people take sides leading to clusters that preferentially propagate information confirming their individual cluster-specific bias. We propose a model for polarized social networks, and show that approaches based on factorizing the matrix of sources and their claims can automate the discovery of polarized clusters with no need for prior training or natural language processing. In turn, identifying such clusters offers insights into prevalent social conflicts and helps automate the generation of less biased descriptions of ongoing events. We evaluate our factorization algorithms and their results on multiple Twitter datasets involving polarization of opinions, demonstrating the efficacy of our approach. Experiments show that our method is almost always correct in identifying the polarized information from real-world twitter traces, and outperforms the baseline mechanisms by a large margin.

Recent work suggested that, in the age of data overload produced by sensors, social media, and IoT devices, a key new type of network transport protocols will be one that offers representative summaries of requested data, retrieved at a consumer-controlled degree of granularity. Given the over-abundance of data, consumers will seldom need all data on a topic, but rather will increasingly favor an appropriate sampling for summarization purposes. The paper explores such sampling as a novel service enabled by information-centric networking paradigms that name data objects, not hosts. By naming data objects, it becomes possible to selectively retrieve them, but the properties of the resulting sampling depend on the naming scheme. This paper describes an automated object naming service, called Espresso, that facilitates content sampling over information- centric networks. We show how Espresso, combined with a trivial retrieval policy, translates the sampling problem into a naming problem, and customizes the naming to different applications' sampling needs. Experimental results show that the computational overhead of automated naming is affordable. The service is first evaluated in simulation, demonstrating a higher sampled-data utility to the consumer, while balancing retrieved data importance and diversity. Social network applications are then introduced, where naming is produced by Espresso. Results demonstrate the advantages of Espresso, compared to baselines, in terms of retrieving meaningful media data summaries.

In this demo, we introduce the tweet-based newsfeed summary service, called iApollo, running on a named data network (NDN) stack. This novel application provides a customized newsfeed service to individual readers based on their interests. Data sampling is essential in iApollo because of the large volume of tweets. Espresso, the automatic naming agent, translates this sampling problem into the simple tree traversal by constructing a hierarchical namespace for the given set of tweets. Two types of tree traversals are introduced: a modified breadth-first-search (BFS) and depth-first-search (DFS) which result in generating headline and complementary news, respectively. This allows readers to quickly achieve the best semantic understanding of the news topic with the minimal number of data retrievals. The newsfeeds are transmitted over NDN because NDN caching can reduce the retrieval delay while releasing burden on the end server. Furthermore, Espresso is well-matched to NDN as it naturally resolves NDN's naming requirement. The application demonstrates not only the tweet-newsfeed service, but also the great synergy between Espresso and NDN. This work can be extended into a general software framework for content summarization.

The paper develops a recursive state estimator for social network data streams that allows exploitation of social networks, such as Twitter, as sensor networks to reliably observe physical events. Recent literature suggested using social networks as sensor networks leveraging the fact that much of the information upload on the former constitutes acts of sensing. A significant challenge identified in that context was that source reliability is often unknown, leading to uncertainty regarding the veracity of reported observations. Multiple truth finding systems were developed to solve this problem, generally geared towards batch analysis of offline datasets. This work complements the present batch approaches by developing an online recursive state estimator that recovers ground truth from streaming data. In this paper, we model physical world state by a set of binary signals (propositions, called assertions, about world state) and the social network as a noisy medium, where distortion, fabrication, omissions, and duplication are introduced. Our recursive state estimator is designed to recover the original binary signal (the true propositions) from the received noisy signal, essentially decoding the unreliable social network output to obtain the best estimate of ground truth in the physical world. Results show that the estimator is both effective and efficient at recovering the original signal with a high degree of accuracy. The estimator gives rise to a novel situation awareness tool that can be used for reliably following unfolding events in real time, using dynamically arriving social network data.

This paper presents experiences with eNav, a smartphone-based vehicular GPS navigation system that has an energy-saving location sensing mode capable of drastically reducing navigation energy needs. Traditional navigation systems sample the phone's GPS at a fixed rate (usually around 1Hz), regardless of factors such as current vehicle speed and distance from the next navigation waypoint. This practice results in a large energy consumption and unnecessarily reduces the attainable length of a navigation session, if the phone is left unplugged. The paper investigates two questions. First, would drivers be willing to sacrifice some of the affordances of modern navigation systems in order to prolong battery life? Second, how much energy could be saved using straightforward alternative localization mechanisms, applied to complement GPS for vehicular navigation? According to a survey we conducted of 500 drivers, as much as 91% of drivers said they would like to have a vehicular navigation application with an energy saving mode. To meet this need, eNav exploits on-board accelerometers for approximate location sensing when the vehicle is sufficiently far from the next navigation waypoint (or is stopped). A user test-study of eNav shows that it results in roughly the same user experience as standard GPS navigation systems, while reducing navigation energy consumption by almost 80%. We conclude that drivers find an energy-saving mode on phone-based vehicular navigation applications desirable, even at the expense of some loss of functionality, and that significant savings can be achieved using straightforward location sensing mechanisms that avoid frequent GPS sampling.

The advent of social networks, mobile sensing, and the Internet of Things herald an age of data overload, where the amount of data generated and stored by various data services exceeds application consumption needs. In such an age, an increasingly important need of data clients will be one of data sub-sampling. This need calls for novel data dissemination protocols that allow clients to request from the network a representative sampling of data that matches a query. In this paper, we present the design of a new transport-layer dissemination protocol, called InfoMax, that allows applications to request such a data sampling. InfoMax exploits the recently proposed named-data-networking (NDN) stack that makes networks aware of hierarchical data names, as opposed to IP addresses. Assuming that named objects with longer prefixes are semantically more similar, InfoMax has the property of minimizing semantic redundancy among delivered data items, hence offering the best coverage of the requested topic with the fewest bytes. The paper discusses the design of InfoMax, its experimental evaluation, and example applications.

The proliferation of mobile sensing and communication devices in the possession of the average individual generated much recent interest in social sensing applications. Significant advances were made on the problem of uncovering ground truth from observations made by participants of unknown reliability. The problem, also called fact-finding commonly arises in applications where unvetted individuals may opt in to report phenomena of interest. For example, reliability of individuals might be unknown when they can join a participatory sensing campaign simply by downloading a smartphone app. This paper extends past social sensing literature by offering a scalable approach for exploiting dependencies between observed variables to increase fact-finding accuracy. Prior work assumed that reported facts are independent, or incurred exponential complexity when dependencies were present. In contrast, this paper presents the first scalable approach for accommodating dependency graphs between observed states. The approach is tested using real-life data collected in the aftermath of hurricane Sandy on availability of gas, food, and medical supplies, as well as extensive simulations. Evaluation shows that combining expected correlation graphs (of outages) with reported observations of unknown reliability, results in a much more reliable reconstruction of ground truth from the noisy social sensing data. We also show that correlation graphs can help test hypotheses regarding underlying causes, when different hypotheses are associated with different correlation patterns. For example, an observed outage profile can be attributed to a supplier outage or to excessive local demand. The two differ in expected correlations in observed outages, enabling joint identification of both the actual outages and their underlying causes.

The paper presents a new model for crowd-sensing applications, where humans are used as the sensing sources to report information regarding the physical world. In contrast to previous work on the topic, we consider a model where the sources in question are polarized. Such might be the case, for example, in political disputes and in situations involving different communities with largely dissimilar beliefs that color their interpretation and reporting of physical world events. Reconstructing accurate ground truth is more complicated when sources are polarized. The paper describes an algorithm that significantly improves the quality of reconstruction results in the presence of polarized sources. For evaluation, we recorded human observations from Twitter for four months during a recent Egyptian uprising against the former president. We then used our algorithm to reconstruct a version of events and compared it to other versions produced by state of the art algorithms. Our analysis of the data set shows the presence of two clearly defined camps in the social network that tend of propagate largely disjoint sets of claims (which is indicative of polarization), as well as third population whose claims overlap subsets of the former two. Experiments show that, in the presence of polarization, our reconstruction tends to align more closely with ground truth in the physical world than the existing algorithms.

This paper complements the large body of social sensing literature by developing means for augmenting sensing data with inference results that "fill-in" missing pieces. Unlike trend-extrapolation methods, we focus on prediction in disaster scenarios where disruptive trend changes occur. A set of prediction heuristics (and a standard trend extrapolation algorithm) are compared that use either predominantly-spatial or predominantly-temporal correlations for data extrapolation purposes. The evaluation shows that none of them do well consistently. This is because monitored system state, in the aftermath of disasters, alternates between periods of relative calm and periods of disruptive change (e.g., aftershocks). A good prediction algorithm, therefore, needs to intelligently combine time-based data extrapolation during periods of calm, and spatial data extrapolation during periods of change. The paper develops such an algorithm. The algorithm is tested using data collected during the New York City crisis in the aftermath of Hurricane Sandy in November 2012. Results show that consistently good predictions are achieved. The work is unique in addressing the bi-modal nature of damage propagation in complex systems subjected to stress, and offers a simple solution to the problem.

In this demo, a learning system, called Metis, is presented that extrapolates missing pieces in participatory sensing data. The work addresses the challenge of incomplete coverage in participatory sensing applications, where lack of complete control over participant mobility and sensing patterns may create coverage gaps in space and in time. Metis learns the underlying spatiotemporal patterns of the measured phenomenon from available incomplete observations, and uses these patterns to infer missing data. We describe the overall system design and demonstrate the system using data collected during the New York City gas crisis in the aftermath of Hurricane Sandy.

This demonstration illustrates a service for collection and delivery of images, in participatory camera networks, to maximize coverage while removing outliers (i.e., irrelevant images). Images, such as those taken by smart-phone users, represent an important and growing modality in social sensing applications. They can be used, for instance, to document occurrences of interest in participatory sensing campaigns, such as instances of graffiti on campus or invasive species in a park. In applications with a significant number of participants, the number of images collected may be very large. A key problem becomes one of data triage to reduce the number of images delivered to a manageable count, without missing important ones. In prior work, the authors presented a service, called PhotoNet [2], that reduces redundancy among delivered images by maximizing diversity. The current work significantly extends our previous effort by recognizing that diversity maximization often leads to selection of outliers; images that are visually different but not necessarily relevant, which in fact reduces the quality of the delivered image pool. We demonstrate a new prioritization technique that maximizes diversity among delivered pictures, while also reducing outliers.

In a wireless ad hoc network, the main issue of a good broadcast protocol is to attain maximum reachability with minimal packet forwarding. Existing protocols address this issue by utilizing the knowledge of up to 2-hop neighbors to approximate an MCDS (minimum connected dominating set) via heuristics derived from techniques known as Self pruning and Dominant pruning. Our experiments show that, using these greedy choice heuristics result in a biased load distribution throughout the network. Some nodes become heavily loaded and consequently packets through those nodes, whether unicast or broadcast, experience significantly larger delay. Contention and collision also increase at some regions, while they are relatively low at other regions. In this paper we address these issues, and propose various methods to evenly distribute the load caused by broadcast packets. Our algorithms take various reactive measures to dynamically include less loaded nodes in the forward list, while maintaining total number of packet forwards low. Detailed simulation using ns-2 shows fair scheduling of resources and significant improvement in distribution of packet forwarding load, packet delay, latency and overall performance.