Quantifying echo chamber effects in information spreading over political communication networks

Echo chambers in online social networks, in which users prefer to interact only with ideologically-aligned peers, are believed to facilitate misinformation spreading and contribute to radicalize political discourse. In this paper, we gauge the effects of echo chambers in information spreading phenomena over political communication networks. Mining 12 million Twitter messages, we reconstruct a network in which users interchange opinions related to the impeachment of the former Brazilian President Dilma Rousseff. We define a continuous {political position} parameter, independent of the network's structure, that allows to quantify the presence of echo chambers in the strongly connected component of the network, reflected in two well-separated communities of similar sizes with opposite views of the impeachment process. By means of simple spreading models, we show that the capability of users in propagating the content they produce, measured by the associated spreadability, strongly depends on their attitude. Users expressing pro-impeachment sentiments are capable to transmit information, on average, to a larger audience than users expressing anti-impeachment sentiments. Furthermore, the users' spreadability is correlated to the diversity, in terms of political position, of the audience reached. Our method can be exploited to identify the presence of echo chambers and their effects across different contexts and shed light upon the mechanisms allowing to break echo chambers.Comment: 9 pages, 4 figures. Supplementary Information available as ancillary fil

Object Detection in Online Proctoring Through Two Camera Using Faster-RCNN

The COVID-19 pandemic has prompted changes in teaching methods from offline to online, including the implementation of exams. But many reports say that the potential for online exam cheating is very high which can compromise the credibility of the exam. The online exam monitoring system using one camera makes it difficult for officers to make decisions because of the lack of evidence and supporting data. In this study, we propose a monitoring approach using two cameras, namely a camera on a laptop to get a front view of the participant and a cellphone camera to get a side view of the examinee but because of the complexity of the problem, at this stage we only focus on the side camera. Implementation begins with the collection of video recording data, custom data sets for training and pretrained datasets from the zoo model. Training is carried out using a custom dataset to detect objects that are not recognized by the pretrained dataset. The evaluation of the training results using the COCO evaluator showed the average of the bbox-AP is 59,169. The fraud detection process is carried out using 6 exam videos with a total of 192,929 frames, producing two outputs, namely object detection videos and csv files. The csv file contains the frame number, time, object detected in each frame. The next process is to analyze the csv file and mark frames that have the potential to be fraudulent. The evaluation results show an accuracy of 0.884615385 and a recall of 0.82142857

enabling a research data management beyond data heterogeneity

A primary goal of a research infrastructure for data management should be to enable efficient data discovery and integration of heterogeneous data. The German Federation for Biological Data (GFBio) was envisioned by this goal. The basic component, that enables such interoperability and serves as a backbone for such a platform, is the GFBio Terminology Service (GFBio TS). It acts as a semantic platform for accessing, developing and reasoning over terminological resources within the biological and environmental domain. A RESTful API gives access to these terminological resources in a uniform way regardless of their degree of complexity and whether they are internally stored or externally accessed through their web services. Additionally, a set of widgets with an intrinsic API connection are made available for an easy integration in applications and web interfaces. Based on the requirements of the GFBio partners, we describe the added value that is provided by the GFBio Terminology Service with practical scenarios but also, what challenges we still face. We conclude by describing our current activities and future developments

Modeling echo chambers and polarization dynamics in social networks

Echo chambers and opinion polarization recently quantified in several sociopolitical contexts and across different social media, raise concerns on their potential impact on the spread of misinformation and on openness of debates. Despite increasing efforts, the dynamics leading to the emergence of these phenomena stay unclear. We propose a model that introduces the dynamics of radicalization, as a reinforcing mechanism driving the evolution to extreme opinions from moderate initial conditions. Inspired by empirical findings on social interaction dynamics, we consider agents characterized by heterogeneous activities and homophily. We show that the transition between a global consensus and emerging radicalized states is mostly governed by social influence and by the controversialness of the topic discussed. Compared with empirical data of polarized debates on Twitter, the model qualitatively reproduces the observed relation between users' engagement and opinions, as well as opinion segregation in the interaction network. Our findings shed light on the mechanisms that may lie at the core of the emergence of echo chambers and polarization in social media

Analiza raspoloženja tvitova predsjednika Trumpa: od pobjede na izborima do borbe protiv COVID-19

Twitter, as one of the popular social networks today and big data generator, can affect and change the public discourse, so political candidates are using it extensively as the vehicle for attracting and keeping their followers. Since Donald Trump\u27s 2016 presidency election, his Twitter account with millions of followers has become an important subject for various statistical analyses, mostly because of his controversy. Therefore, this paper uses sentiment analysis of a large set of his tweets to explore his influence, as well the set of affective and cognitive aspects of his messages. The results of this analysis indicate what kind of findings in political domain can be recognized from tweets, and how they can be interpreted.Twitter, kao jedna od popularnih društvenih mreža današnjice i generator velikih podataka, može utjecati i mijenjati javni diskurs, pa ga politički kandidati intenzivno koriste kao sredstvo za privlačenje i održavanje pinga svojih pratitelja. Od predsjedničkih izbora Donalda Trumpa 2016., njegov Twitter račun s milijunima pratitelja postao je važan predmet raznih statističkih analiza, ponajviše zbog njegove kontroverze. Stoga ovaj rad koristi analizu sentimenta velikog skupa njegovih tweetova kako bi istražio njegov utjecaj, kao i skup afektivnih i kognitivnih aspekata njegovih poruka. Rezultati ove analize ukazuju na to kakva se saznanja u političkoj domeni mogu prepoznati iz tweetova i kako ih se može interpretirati

Adapting Collaborative Chat for Massive Open Online Courses: Lessons Learned

Abstract. In this paper we explore how to import intelligent support for group learning that has been demonstrated as effective in classroom instruction into a Massive Open Online Course (MOOC) context. The Bazaar agent architecture paired with an innovative Lobby tool to enable coordination for synchronous reflection exercises provides a technical foundation for our work. We describe lessons learned, directions for future work, and offer pointers to resources for other researchers interested in computer supported collaborative learning in MOOCs

Cognitive Representation Learning of Self-Media Online Article Quality

The automatic quality assessment of self-media online articles is an urgent and new issue, which is of great value to the online recommendation and search. Different from traditional and well-formed articles, self-media online articles are mainly created by users, which have the appearance characteristics of different text levels and multi-modal hybrid editing, along with the potential characteristics of diverse content, different styles, large semantic spans and good interactive experience requirements. To solve these challenges, we establish a joint model CoQAN in combination with the layout organization, writing characteristics and text semantics, designing different representation learning subnetworks, especially for the feature learning process and interactive reading habits on mobile terminals. It is more consistent with the cognitive style of expressing an expert's evaluation of articles. We have also constructed a large scale real-world assessment dataset. Extensive experimental results show that the proposed framework significantly outperforms state-of-the-art methods, and effectively learns and integrates different factors of the online article quality assessment.Comment: Accepted at the Proceedings of the 28th ACM International Conference on Multimedi

The role of context in the nature and development of DIY impact measurement tools: a case study

This article aims to explore the nature of do-it-yourself (DIY) impact measurement tools used in the voluntary sector, using a contextual inquiry approach. This is an understudied area of research, knowledge that would be considerably valuable for practitioners in the sector who wish to create their own DIY impact measurement tool. Semi-structured interviews and observation sessions are used to explore an example of a DIY impact measurement tool, the processes of its creation and operation, and how it has been shaped, from the perspective of a UK environmental charity. The study identifies how and why the tool was created as well as which resources are being used to build it. Findings show that the functionality and requirements of the DIY impact measurement tool are mostly shaped by the charity’s social, cultural and organisational characteristics