1,720,981 research outputs found
Continuous authentication through gait analysis on a wrist-worn device
No full textBeing distinctive of every individual, gait can be used as a biometric feature to authenticate the owner of a wearable device. This paper proposes and evaluates an authentication method that relies on the acceleration signal acquired at the user's wrist. During the training phase, the wrist-worn device automatically learns the gait patterns of the legitimate user, by exploiting a set of acceleration-based indicators. Subsequently, unauthorized users are detected by observing the occurrence of anomalous gait patterns. Experimental results carried out with 20 volunteers show that the proposed method is able to recognize the legitimate user with an equal error rate of ∼2.5%. The method is sufficiently lightweight to be executed in real time on a wearable device with limited resources. This enables continuous authentication without requiring the presence of an external device (e.g., a smartphone). Furthermore, the provided evaluation of power consumption shows that the completely on-node solution is also more energy efficient with respect to off-loading computation to an external device
Detecting inorganic financial campaigns on Twitter
No full textOnline financial content is widespread on social media, especially on Twitter. The possibility to access open, real-time data about stock market information and firms’ public reputation can bring competitive advantages to industry insiders. However, as many studies extensively demonstrated before, manipulative campaigns by social bots do not spare the financial sector either.
In this work, we show that the more viral a stock is on Twitter, the more that virality is artificially caused by social bots. This result is also confirmed when considering accounts suspended by Twitter instead of bots. Starting from this finding, we then propose two methods for detecting the presence and the extent of financial disinformation on Twitter, via classification and regression. Our systems exploit hundreds of features to encode the characteristics of viral discussions, including features about: participating users, textual content of shared posts, temporal patterns of diffusion, and financial information about stocks. We experiment with different combinations of algorithms and features, achieving excellent results for the detection of financial disinformation (F1 = 0.97) and promising results for the challenging task of estimating the extent of inorganic activity within financial discussions (R2 = 0.81, MAE = 4.9%). Our compelling results pave the way for the deployment of novel systems for protecting against financial disinformation
MARS, a multi-agent system for assessing rowers' coordination via motion-based stigmergy
No full textA crucial aspect in rowing is having a synchronized, highly-efficient stroke. This is very difficult to obtain, due to the many interacting factors that each rower of the crew must perceive. Having a system that monitors and represents the crew coordination would be of great help to the coach during training sessions. In the literature, some methods already employ wireless sensors for capturing motion patterns that affect rowing performance. A challenging problem is to support the coach's decisions at his same level of knowledge, using a limited number of sensors and avoiding the complexity of the biomechanical analysis of human movements. In this paper, we present a multi-agent information-processing system for on-water measuring of both the overall crew asynchrony and the individual rower asynchrony towards the crew. More specifically, in the system, the first level of processing is managed by marking agents, which release marks in a sensing space, according to the rowers' motion. The accumulation of marks enables a stigmergic cooperation mechanism, generating collective marks, i.e., short-term memory structures in the sensing space. At the second level of processing, information provided by marks is observed by similarity agents, which associate a similarity degree with respect to optimal marks. Finally, the third level is managed by granulation agents, which extract asynchrony indicators for different purposes. The effectiveness of the system has been experimented on real-world scenarios. The study includes the problem statement and its characterization in the literature, as well as the proposed solving approach and initial experimental setting
Characterizing Social Bots Spreading Financial Disinformation
No full textDespite the existence of several studies on the characteristics and role of social bots in spreading disinformation related to politics, health, science and education, financial social bots remain a largely unexplored topic. We aim to shed light on this issue by investigating the activities of large social botnets in Twitter, involved in discussions about stocks traded in the main US financial markets. We show that the largest discussion spikes are in fact caused by mass-retweeting bots. Then, we focus on characterizing the activity of these financial bots, finding that they are involved in speculative campaigns aimed at promoting low-value stocks by exploiting the popularity of high-value ones. We conclude by highlighting the peculiar features of these accounts, comprising similar account creation dates, similar screen names, biographies, and profile pictures. These accounts appear as untrustworthy and quite simplistic bots, likely aiming to fool automatic trading algorithms rather than human investors. Our findings pave the way for the development of accurate detection and filtering techniques for financial spam. In order to foster research and experimentation on this novel topic, we make our dataset publicly available for research purposes
Investigating the difference between trolls, social bots, and humans on Twitter
Get PDFIt has become apparent that human accounts are not the sole actors in the social media scenario. The expanding role of social media in the consumption and diffusion of information has been accompanied by attempts to influence public opinion. Researchers reported several instances where social bots, automated accounts designed to impersonate humans, have been deployed for this purpose. More recently, platforms such as Twitter provided evidence pointing to governments creating and using fake accounts in this kind of abuse. These accounts are known as state-backed trolls. Although these different actors have been widely studied, there is little understanding of how they differ when examined together. In this paper, we contribute to understanding the characteristics of the different types of accounts and increase our awareness of Twitter’s state-backed trolls, which so far have received limited attention from quantitative researchers. We propose a large-scale quantitative analysis, which relies on both datasets released by Twitter and researchers in recent years to characterize the different actors that take part in the social network scenario. In particular, we represent each account with a large number of features categorized into three distinct traits: credibility, initiative, and adaptability concerning the underlying aspects into which they best fit. We conducted subsequent experiments, isolating features on their respective traits and using them all. First, we apply dimensionality reduction to project accounts onto the same bi-dimensional space and visualize how they distribute across it. Then, we experiment with different combinations of two parameters that affect the dimensionality reduction and clustering algorithm to find which trait is best suited to distinguish the different actors. In our best combination in terms of effectiveness, we obtain high-quality clusters, achieving a purity score of 0.9, which results in homogeneous clusters where accounts of the same category are grouped. Beyond that, we explore our results by visualizing and studying clustering results to determine the differences between account categories. Using our defined traits, we show that it is possible to distinguish the different accounts through clustering, obtaining the best results while leveraging the three traits simultaneously. An additional analysis shows that features related to retweeting patterns and URLs sharing are effective in differentiating trolls and humans. At the same time, social bot accounts suffer from recall degradation in cross-domain evaluation. Moreover, we show that accounts belonging to the same dataset are not necessarily similar in the defined traits. Finally, we perform a feature importance analysis using SHAP to gain insights into which features best differentiate the account when examined in pairs
RTbust: Exploiting temporal patterns for botnet detection on twitter
No full textWithin OSNs, many of our supposedly online friends may instead be fake accounts called social bots, part of large groups that purposely re-share targeted content. Here, we study retweeting behaviors on Twitter, with the ultimate goal of detecting retweeting social bots. We collect a dataset of 10M retweets. We design a novel visualization that we leverage to highlight benign and malicious patterns of retweeting activity. In this way, we uncover a "normal" retweeting pattern that is peculiar of human-operated accounts, and 3 suspicious patterns related to bot activities. Then, we propose a bot detection technique that stems from the previous exploration of retweeting behaviors. Our technique, called Retweet-Buster (RTbust), leverages unsupervised feature extraction and clustering. An LSTM autoencoder converts the retweet time series into compact and informative latent feature vectors, which are then clustered with a hierarchical density-based algorithm. Accounts belonging to large clusters characterized by malicious retweeting patterns are labeled as bots. RTbust obtains excellent detection results, with F 1 = 0.87, whereas competitors achieve F 1 ≤ 0.76. Finally, we apply RTbust to a large dataset of retweets, uncovering 2 previously unknown active botnets with hundreds of accounts
The Great Ban: Efficacy and Unintended Consequences of a Massive Deplatforming Operation on Reddit
No full textIn the current landscape of online abuses and harms, effective content moderation is necessary to cultivate safe and inclusive online spaces. Yet, the effectiveness of many moderation interventions is still unclear. Here, we assess the effectiveness of The Great Ban, a massive deplatforming operation that affected nearly 2,000 communities on Reddit. By analyzing 16M comments posted by 17K users during 14 months, we provide nuanced results on the effects-both desired and otherwise-of the ban. Among our main findings is that 15.6% of the affected users left Reddit and that those who remained reduced their toxicity by 6.6% on average. The ban also caused 5% users to increase their toxicity by more than 70% of their pre-ban level. Overall, our multifaceted results provide new insights into the efficacy of deplatforming. As such, our findings can inform the development of future moderation interventions and the policing of online platforms
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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