Social media has become one of the main channels for people to access and consume news, due to the rapidness and low cost of news dissemination on it. However, such properties of social media also make it a hotbed of fake news dissemination, bringing negative impacts on both individuals and society. Therefore, detecting fake news has become a crucial problem attracting tremendous research effort. Most existing methods of fake news detection are supervised, which require an extensive amount of time and labor to build a reliably annotated dataset. In search of an alternative, in this paper, we investigate if we could detect fake news in an unsupervised manner. We treat truths of news and users’ credibility as latent random variables, and exploit users’ engagements on social media to identify their opinions towards the authenticity of news. We leverage a Bayesian network model to capture the conditional dependencies among the truths of news, the users’ opinions, and the users’ credibility. To solve the inference problem, we propose an efficient collapsed Gibbs sampling approach to infer the truths of news and the users’ credibility without any labelled data. Experiment results on two datasets show that the proposed method significantly outperforms the compared unsupervised methods.

With the growing deployment of wireless communication technologies, radio spectrum is becoming a scarce resource. Thus mechanisms to efficiently allocate the available spectrum are of interest. In this paper, we model the radio spectrum allocation problem as a sealed-bid reserve auction, and propose SMALL, which is a Strategy-proof Mechanism for radio spectrum ALLocation. Furthermore, we extend SMALL to adapt to multi-radio spectrum buyers, which can bid for more than one radio.

In the problem of routing in multi-hop wireless networks, to achieve high end-to-end throughput, it is crucial to find the "best" path from the source node to the destination node. Although a large number of routing protocols have been proposed to find the path with minimum total transmission count/time for delivering a single packet, such transmission count/time minimizing protocols cannot be guaranteed to achieve maximum end-to-end throughput. In this paper, we argue that by carefully considering spatial reusability of the wireless communication media, we can tremendously improve the end-to-end throughput in multi-hop wireless networks. To support our argument, we propose spatial reusability-aware single-path routing (SASR) and anypath routing (SAAR) protocols, and compare them with existing single-path routing and anypath routing protocols, respectively. Our evaluation results show that our protocols significantly improve the end-to-end throughput compared with existing protocols. Specifically, for single-path routing, the median throughput gain is up to 60%, and for each source-destination pair, the throughput gain is as high as 5.3x; for anypath routing, the maximum per-flow throughput gain is 71.6%, while the median gain is up to 13.2%.existing routing protocols, no matter single-path routing protocols or anypath routing protocols, rely on linkquality aware routing metrics, such as link transmission count-based metrics (e.g., ETX [6] and EATX [32]) and link transmission time-based metrics (e.g., ETT [7] and EATT [13]). They simply select the (any)path that minimizes the overall transmission counts or transmission time for delivering a packet.However, an important property of the wireless communication media, which distinguishes it from traditional wired communication media, is the spatial reusability. Specifically, because wireless signals fade during propagation, two links are free of interference if they are far away enough, and thus can transmit at the same time on the same channel. To the best of our knowledge, most of the existing routing protocols do not take spatial reusability of the wireless communication media into account. Our example in Section 3.2 will show the improper usage of routing metrics by existing routing protocols, when spectrum spatial reusability is not considered. In this primer work, we argue that by carefully considering spatial reusability of the wireless communication media, we can tremendously improve the end-to-end throughput in multi-hop wireless networks (i.e., up to 5.3× throughput gain in single-path routing and up to 71.6% gain in anypath routing shown by our evaluation results).The detailed contributions of our work are as follows.• To the best of our knowledge, we are the first to explicitly consider spatial reusability of the wireless communication media in routing, and design practical spatial reusability-aware single-path routing (SASR) and anypath routing (SAAR) protocols. • We formulate the problem of spatial reusability-0018-9340 (c)

Incentive mechanisms for crowdsourcing have been extensively studied under the framework of all-pay auctions. Along a distinct line, this paper proposes to use Tullock contests as an alternative tool to design incentive mechanisms for crowdsourcing. We are inspired by the conduciveness of Tullock contests to attracting user entry (yet not necessarily a higher revenue) in other domains. In this paper, we explore a new dimension in optimal Tullock contest design, by superseding the contest prize---which is fixed in conventional Tullock contests---with a prize function that is dependent on the (unknown) winner's contribution, in order to maximize the crowdsourcer's utility. We show that this approach leads to attractive practical advantages: (a) it is well-suited for rapid prototyping in fully distributed web agents and smartphone apps; (b) it overcomes the disincentive to participate caused by players' antagonism to an increasing number of rivals. Furthermore, we optimize conventional, fixed-prize Tullock contests to construct the most superior benchmark to compare against our mechanism. Through extensive evaluations, we show that our mechanism significantly outperforms the optimal benchmark, by over three folds on the crowdsourcer's utility cum profit and up to nine folds on the players' social welfare.Comment: 9 pages, 4 figures, 3 table

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