Social velocity based spatio-temporal anomalous daily activity discovery of social media users

Author(s):  
Ahmet Sakir Dokuz
2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Yasmeen George ◽  
Shanika Karunasekera ◽  
Aaron Harwood ◽  
Kwan Hui Lim

AbstractA key challenge in mining social media data streams is to identify events which are actively discussed by a group of people in a specific local or global area. Such events are useful for early warning for accident, protest, election or breaking news. However, neither the list of events nor the resolution of both event time and space is fixed or known beforehand. In this work, we propose an online spatio-temporal event detection system using social media that is able to detect events at different time and space resolutions. First, to address the challenge related to the unknown spatial resolution of events, a quad-tree method is exploited in order to split the geographical space into multiscale regions based on the density of social media data. Then, a statistical unsupervised approach is performed that involves Poisson distribution and a smoothing method for highlighting regions with unexpected density of social posts. Further, event duration is precisely estimated by merging events happening in the same region at consecutive time intervals. A post processing stage is introduced to filter out events that are spam, fake or wrong. Finally, we incorporate simple semantics by using social media entities to assess the integrity, and accuracy of detected events. The proposed method is evaluated using different social media datasets: Twitter and Flickr for different cities: Melbourne, London, Paris and New York. To verify the effectiveness of the proposed method, we compare our results with two baseline algorithms based on fixed split of geographical space and clustering method. For performance evaluation, we manually compute recall and precision. We also propose a new quality measure named strength index, which automatically measures how accurate the reported event is.


Author(s):  
Mél Hogan

We often think of surveillance as ubiquitous, secretive, top-down, corporate, and governmental—and in many ways, it is. Through three vignettes, this essay prods at the ways in which our everyday tools, technologies, and gestures extend surveillance’s reach into our intimate lives and relationships. Each vignette is a story constructed from facts gleaned in news stories, social media, or personal conversations. As such, these vignettes are neither empirical nor entirely speculative. In an effort to consider surveillance as an ongoing and daily activity, they invite readers into more intimate contexts than those that are usually the object of rigorous scholarly analysis. In their intimacy, these stories serve to remind us of the ways in which communication devices are always, in some capacity, tracking and trailing our desires. Vignette 1 tells the story of the NSA agent who uses the agency’s powerful database to spy on an ex-lover. Vignette 2 explores the kinds of information users can get (about themselves) from Big Tech companies, from social media and dating apps. Vignette 3 looks at Internet cookies and their capacity to make unlikely—and unwanted—introductions. Technology, apps, and our always-on devices complicate the boundaries of intimacy and often work to redefine the trajectories of our desire in the process. The breaches of trust detailed in these stories expose the ways in which Big Tech’s desire to predict and to measure human emotion and behaviour exists in tension with our memories, our secrets, and our wild imaginations.


Author(s):  
F. O. Ostermann ◽  
H. Huang ◽  
G. Andrienko ◽  
N. Andrienko ◽  
C. Capineri ◽  
...  

Increasing availability of Geo-Social Media (e.g. Facebook, Foursquare and Flickr) has led to the accumulation of large volumes of social media data. These data, especially geotagged ones, contain information about perception of and experiences in various environments. Harnessing these data can be used to provide a better understanding of the semantics of places. We are interested in the similarities or differences between different Geo-Social Media in the description of places. This extended abstract presents the results of a first step towards a more in-depth study of semantic similarity of places. Particularly, we took places extracted through spatio-temporal clustering from one data source (Twitter) and examined whether their structure is reflected semantically in another data set (Flickr). Based on that, we analyse how the semantic similarity between places varies over space and scale, and how Tobler's first law of geography holds with regards to scale and places.


Author(s):  
Junfang Gong ◽  
Runjia Li ◽  
Hong Yao ◽  
Xiaojun Kang ◽  
Shengwen Li

The human daily activity category represents individual lifestyle and pattern, such as sports and shopping, which reflect personal habits, lifestyle, and preferences and are of great value for human health and many other application fields. Currently, compared to questionnaires, social media as a sensor provides low-cost and easy-to-access data sources, providing new opportunities for obtaining human daily activity category data. However, there are still some challenges to accurately recognizing posts because existing studies ignore contextual information or word order in posts and remain unsatisfactory for capturing the activity semantics of words. To address this problem, we propose a general model for recognizing the human activity category based on deep learning. This model not only describes how to extract a sequence of higher-level word phrase representations in posts based on the deep learning sequence model but also how to integrate temporal information and external knowledge to capture the activity semantics in posts. Considering that no benchmark dataset is available in such studies, we built a dataset that was used for training and evaluating the model. The experimental results show that the proposed model significantly improves the accuracy of recognizing the human activity category compared with traditional classification methods.


Author(s):  
Nadav Hochman ◽  
Lev Manovich

How are users’ experiences of production, sharing, and interaction with the media they create mediated by the interfaces of particular social media platforms? How can we use computational analysis and visualizations of the content of visual social media (e.g., user photos, as opposed to upload dates, locations, tags and other metadata) to study social and cultural patterns? How can we visualize this media on multiple spatial and temporal scales? In this paper, we examine these questions through the analysis of the popular mobile photo–sharing application Instagram. First, we analyze the affordances provided by the Instagram interface and the ways this interface and the application’s tools structure users’ understanding and use of the “Instagram medium.” Next, we compare the visual signatures of 13 different global cities using 2.3 million Instagram photos from these cities. Finally, we use spatio–temporal visualizations of over 200,000 Instagram photos uploaded in Tel Aviv, Israel over three months to show how they can offer social, cultural and political insights about people’s activities in particular locations and time periods.


2014 ◽  
Vol 143 ◽  
pp. 144-152 ◽  
Author(s):  
Dan Xu ◽  
Rui Song ◽  
Xinyu Wu ◽  
Nannan Li ◽  
Wei Feng ◽  
...  

2020 ◽  
Vol 34 (5) ◽  
pp. 1267-1290
Author(s):  
Xin Du ◽  
Yulong Pei ◽  
Wouter Duivesteijn ◽  
Mykola Pechenizkiy

Abstract Collective social media provides a vast amount of geo-tagged social posts, which contain various records on spatio-temporal behavior. Modeling spatio-temporal behavior on collective social media is an important task for applications like tourism recommendation, location prediction and urban planning. Properly accomplishing this task requires a model that allows for diverse behavioral patterns on each of the three aspects: spatial location, time, and text. In this paper, we address the following question: how to find representative subgroups of social posts, for which the spatio-temporal behavioral patterns are substantially different from the behavioral patterns in the whole dataset? Selection and evaluation are the two challenging problems for finding the exceptional subgroups. To address these problems, we propose BNPM: a Bayesian non-parametric model, to model spatio-temporal behavior and infer the exceptionality of social posts in subgroups. By training BNPM on a large amount of randomly sampled subgroups, we can get the global distribution of behavioral patterns. For each given subgroup of social posts, its posterior distribution can be inferred by BNPM. By comparing the posterior distribution with the global distribution, we can quantify the exceptionality of each given subgroup. The exceptionality scores are used to guide the search process within the exceptional model mining framework to automatically discover the exceptional subgroups. Various experiments are conducted to evaluate the effectiveness and efficiency of our method. On four real-world datasets our method discovers subgroups coinciding with events, subgroups distinguishing professionals from tourists, and subgroups whose consistent exceptionality can only be truly appreciated by combining exceptional spatio-temporal and exceptional textual behavior.


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