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Register a new userAnd Now for Something Completely Different: Visual Novelty in an Online Network of Designers
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Added by
Balint Daroczy
Publication date
2018
fields
Informatics Engineering
and research's language is
English
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No Arabic abstract
Novelty is a key ingredient of innovation but quantifying it is difficult. This is especially true for visual work like graphic design. Using designs shared on an online social network of professional digital designers, we measure visual novelty using statistical learning methods to compare an images features with those of images that have been created before. We then relate social network position to the novelty of the designers images. We find that on this professional platform, users with dense local networks tend to produce more novel but generally less successful images, with important exceptions. Namely, users making novel images while embedded in cohesive local networks are more successful.
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Visual analysis of temporal networks comprises an effective way to understand the network dynamics, facilitating the identification of patterns, anomalies, and other network properties, thus resulting in fast decision making. The amount of data in real-world networks, however, may result in a layout with high visual clutter due to edge overlapping. This is particularly relevant in the so-called streaming networks, in which edges are continuously arriving (online) and in non-stationary distribution. All three network dimensions, namely node, edge, and time, can be manipulated to reduce such clutter and improve readability. This paper presents an online and nonuniform timeslicing method, thus considering the underlying network structure and addressing streaming network analyses. We conducted experiments using two real-world networks to compare our method against uniform and nonuniform timeslicing strategies. The results show that our method automatically selects timeslices that effectively reduce visual clutter in periods with bursts of events. As a consequence, decision making based on the identification of global temporal patterns becomes faster and more reliable.
Massive amounts of fake news and conspiratorial content have spread over social media before and after the 2016 US Presidential Elections despite intense fact-checking efforts. How do the spread of misinformation and fact-checking compete? What are the structural and dynamic characteristics of the core of the misinformation diffusion network, and who are its main purveyors? How to reduce the overall amount of misinformation? To explore these questions we built Hoaxy, an open platform that enables large-scale, systematic studies of how misinformation and fact-checking spread and compete on Twitter. Hoaxy filters public tweets that include links to unverified claims or fact-checking articles. We perform k-core decomposition on a diffusion network obtained from two million retweets produced by several hundred thousand accounts over the six months before the election. As we move from the periphery to the core of the network, fact-checking nearly disappears, while social bots proliferate. The number of users in the main core reaches equilibrium around the time of the election, with limited churn and increasingly dense connections. We conclude by quantifying how effectively the network can be disrupted by penalizing the most central nodes. These findings provide a first look at the anatomy of a massive online misinformation diffusion network.
Parler is as an alternative social network promoting itself as a service that allows to speak freely and express yourself openly, without fear of being deplatformed for your views. Because of this promise, the platform become popular among users who were suspended on mainstream social networks for violating their terms of service, as well as those fearing censorship. In particular, the service was endorsed by several conservative public figures, encouraging people to migrate from traditional social networks. After the storming of the US Capitol on January 6, 2021, Parler has been progressively deplatformed, as its app was removed from Apple/Google Play stores and the website taken down by the hosting provider. This paper presents a dataset of 183M Parler posts made by 4M users between August 2018 and January 2021, as well as metadata from 13.25M user profiles. We also present a basic characterization of the dataset, which shows that the platform has witnessed large influxes of new users after being endorsed by popular figures, as well as a reaction to the 2020 US Presidential Election. We also show that discussion on the platform is dominated by conservative topics, President Trump, as well as conspiracy theories like QAnon.
Classification problems have made significant progress due to the maturity of artificial intelligence (AI). However, differentiating items from categories without noticeable boundaries is still a huge challenge for machines -- which is also crucial for machines to be intelligent. In order to study the fuzzy concept on classification, we define and propose a globalness detection with the four-stage operational flow. We then demonstrate our framework on Facebook public pages inter-like graph with their geo-location. Our prediction algorithm achieves high precision (89%) and recall (88%) of local pages. We evaluate the results on both states and countries level, finding that the global node ratios are relatively high in those states (NY, CA) having large and international cities. Several global nodes examples have also been shown and studied in this paper. It is our hope that our results unveil the perfect value from every classification problem and provide a better understanding of global and local nodes in Online Social Networks (OSNs).
Online social networks have become incredibly popular in recent years, which prompts an increasing number of companies to promote their brands and products through social media. This paper presents an approach for identifying influential nodes in online social network for brand communication. We first construct a weighted network model for the users and their relationships extracted from the brand-related contents. We quantitatively measure the individual value of the nodes in the community from both the network structure and brand engagement aspects. Then an algorithm for identifying the influential nodes from the virtual brand community is proposed. The algorithm evaluates the importance of the nodes by their individual values as well as the individual values of their surrounding nodes. We extract and construct a virtual brand community for a specific brand from a real-life online social network as the dataset and empirically evaluate the proposed approach. The experimental results have shown that the proposed approach was able to identify influential nodes in online social network. We can get an identification result with higher ratio of verified users and user coverage by using the approach.
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