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Register a new userMotivations for Participation in Socially Networked Collective Intelligence Systems
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One of the most significant challenges facing systems of collective intelligence is how to encourage participation on the scale required to produce high quality data. This paper details ongoing work with Phrase Detectives, an online game-with-a-purpose deployed on Facebook, and investigates user motivations for participation in social network gaming where the wisdom of crowds produces useful data.
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The emergence and ongoing development of Web 2.0 technologies have enabled new and advanced forms of collective intelligence at unprecedented scales, allowing large numbers of individuals to act collectively and create high quality intellectual artifacts. However, little is known about how and when they indeed promote collective intelligence. In this manuscript, we provide a survey of the automated tools developed to analyze discourse-centric collective intelligence. By conducting a thematic analysis of the current research direction, a set of gaps and limitations are identified.
This research addresses recommending presentation sessions at smart conferences to participants. We propose a venue recommendation algorithm, Socially-Aware Recommendation of Venues and Environments (SARVE). SARVE computes correlation and social characteristic information of conference participants. In order to model a recommendation process using distributed community detection, SARVE further integrates the current context of both the smart conference community and participants. SARVE recommends presentation sessions that may be of high interest to each participant. We evaluate SARVE using a real world dataset. In our experiments, we compare SARVE to two related state-of-the-art methods, namely: Context-Aware Mobile Recommendation Services (CAMRS) and Conference Navigator (Recommender) Model. Our experimental results show that in terms of the utilized evaluation metrics: precision, recall, and f-measure, SARVE achieves more reliable and favorable social (relations and context) recommendation results.
A commonly used method to protect user privacy in data collection is to perform randomized perturbation on users real data before collection so that aggregated statistics can still be inferred without endangering secrets held by individuals. In this paper, we take a closer look at the validity of Differential Privacy guarantees, when the sensitive attributes are subject to social influence and contagions. We first show that in the absence of any knowledge about the contagion network, an adversary that tries to predict the real values from perturbed ones, cannot achieve an area under the ROC curve (AUC) above $1-(1-delta)/(1+e^varepsilon)$, if the dataset is perturbed using an $(varepsilon,delta)$-differentially private mechanism. Then, we show that with the knowledge of the contagion network and model, one can do significantly better. We demonstrate that our method passes the performance limit imposed by differential privacy. Our experiments also reveal that nodes with high influence on others are at more risk of revealing their secrets than others. The performance is shown through extensive experiments on synthetic and real-world networks.
We use a method based on machine learning, big-data analytics, and network theory to process millions of messages posted in Twitter to predict election outcomes. The model has achieved accurate results in the current Argentina primary presidential election on August 11, 2019 by predicting the large difference win of candidate Alberto Fernandez over president Mauricio Macri; a result that none of the traditional pollsters in that country was able to predict, and has led to a major bond market collapse. We apply the model to the upcoming Argentina presidential election on October 27, 2019 yielding the following results: Fernandez 47.5%, Macri 30.9% and third party 21.6%. Our method improves over traditional polling methods which are based on direct interactions with small number of individuals that are plagued by ever declining response rates, currently falling in the low single digits. They provide a reliable polling method that can be applied not only to predict elections but to discover any trend in society, for instance, what people think about climate change, politics or education.
We study collective attention paid towards hurricanes through the lens of $n$-grams on Twitter, a social media platform with global reach. Using hurricane name mentions as a proxy for awareness, we find that the exogenous temporal dynamics are remarkably similar across storms, but that overall collective attention varies widely even among storms causing comparable deaths and damage. We construct `hurricane attention maps and observe that hurricanes causing deaths on (or economic damage to) the continental United States generate substantially more attention in English language tweets than those that do not. We find that a hurricanes Saffir-Simpson wind scale category assignment is strongly associated with the amount of attention it receives. Higher category storms receive higher proportional increases of attention per proportional increases in number of deaths or dollars of damage, than lower category storms. The most damaging and deadly storms of the 2010s, Hurricanes Harvey and Maria, generated the most attention and were remembered the longest, respectively. On average, a category 5 storm receives 4.6 times more attention than a category 1 storm causing the same number of deaths and economic damage.
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