Construction and Reconstruction of Social Event Sequences: A Developmental and Comparative Study

1985 ◽  
Vol 56 (2) ◽  
pp. 511-518 ◽  
Author(s):  
Louis Oppenheimer ◽  
Monique Groot
Keyword(s):  
Comparative Study ◽  
Cognitive Ability ◽  
Event Sequence ◽  
Guttman Scale ◽  
Social Event ◽  
Event Sequences ◽  
Initial Event ◽  
Social Events

78 children from three age levels ( M ages 5.7, 7.5, and 9.0 yr.) and from Dutch and nonDutch parents were presented three tasks to index their abilities to construct and reconstruct sequences of social events. The results show that all tasks form a stochastic Guttman-scale indicating that all measures tap the same cognitive ability. The resulting hierarchy of the tasks suggests that the ability to complete an event sequence by selecting from two additional events, i.e., the initial and the final events, preceded ability to select from two alternative events either the correct final or the correct initial event. The latter two constructional abilities preceded ability to reconstruct sequences of social events involving memorizing the sequence and anticipatory and reversibility competences.

Social Perspective-Taking and Event Sequences: A Developmental Study

1983 ◽  
Vol 53 (3) ◽  
pp. 683-690 ◽  
Author(s):  
Louis Oppenheimer ◽  
Huub Van Der Lee
Keyword(s):  
Perspective Taking ◽  
Social Reasoning ◽  
Developmental Study ◽  
Social Perspective ◽  
Social Event ◽  
Social Perspective Taking ◽  
Event Sequences ◽  
Social Events

The purposes of this study were to examine the relationships between abilities to reconstruct sequences of social events, competences in social perspective-taking, and conservation skill as an index of the reversibility operation. It was expected that conservation (i.e., the reversibility operation) would developmentally precede competence in both forward and backward temporal reconstruction and would be conditional to the latter; that forward temporal reconstruction would develop prior to backward temporal reconstruction; and that both reconstruction abilities would be prerequisites to competences in social perspective-taking. Three groups of 16 children each, whose mean ages were 5.5, 6.8, and 8.7 yr., were presented tasks assessing the above abilities. Analysis indicated that reconstruction of social-event sequences precedes solutions in social and non-social reasoning.

scholarly journals A Variational Point Process Model for Social Event Sequences

2020 ◽  
Vol 34 (01) ◽  
pp. 173-180
Author(s):  
Zhen Pan ◽  
Zhenya Huang ◽  
Defu Lian ◽  
Enhong Chen
Keyword(s):  
Point Process ◽  
Real World ◽  
Process Model ◽  
Classification Model ◽  
Event Sequence ◽  
Event Sequences ◽  
Sequence Modeling ◽  
Stochastic Point Process ◽  
Feature Based ◽  
Real World Datasets

Many events occur in real-world and social networks. Events are related to the past and there are patterns in the evolution of event sequences. Understanding the patterns can help us better predict the type and arriving time of the next event. In the literature, both feature-based approaches and generative approaches are utilized to model the event sequence. Feature-based approaches extract a variety of features, and train a regression or classification model to make a prediction. Yet, their performance is dependent on the experience-based feature exaction. Generative approaches usually assume the evolution of events follow a stochastic point process (e.g., Poisson process or its complexer variants). However, the true distribution of events is never known and the performance depends on the design of stochastic process in practice. To solve the above challenges, in this paper, we present a novel probabilistic generative model for event sequences. The model is termed Variational Event Point Process (VEPP). Our model introduces variational auto-encoder to event sequence modeling that can better use the latent information and capture the distribution over inter-arrival time and types of event sequences. Experiments on real-world datasets prove effectiveness of our proposed model.

scholarly journals Sensing And Detecting Small-Scale Events Using Geosocial Media Data

2021 ◽  
Author(s):  
Shishuo Xu
Keyword(s):  
Social Media ◽  
Event Detection ◽  
Human Movement ◽  
Small Scale ◽  
Type I ◽  
Social Event ◽  
Ground Truth Data ◽  
Social Events ◽  
Social Media Platforms ◽  
Media Data

<div>Small-scale events involve interactive human movement in limited space and time. Social media platforms possibly generate large amount of geospatially-referenced information related to small-scale events. It benefits individuals, management departments, and urban systems if small-scale events can be timely detected from social media platforms, where measuring the abnormal patterns of human movement to discover events and analyzing associated texts to interpret the reasons behind abnormal movement are two keys. Through investigating how people move as different events occur and measuring the patterns on social media platforms, small-scale events can be generally classified into two types, namely type I events with abrupt patterns and type II events with random occurrence of key factors, where social events and traffic events are representative correspondingly.</div><div>Despite many studies have been conducted to detect social events and traffic events using geosocial media data, there still are some un-answered questions requiring further research. Most existing studies did not identify occurring events from a full coverage of spatial, temporal, and semantic perspectives. Studies concerning social event detection lack efficient semantic analysis summarizing event content to infer the reasons driving the abnormal movement. The typical classification-based method regarding traffic event detection lacks investigation on how the spatiotemporal distribution of traffic relevant posts associate with the occurring traffic events, and simply assigns the detected events with predefined categories, missing events that indicate traffic anomalies but go beyond the predetermined categories.<br></div><div>In this thesis, spatial-temporal-semantic approaches are proposed to measure spatiotemporal patterns of posts and users of social media platforms to capture abnormal human movement, and analyze the content of associated posts to mine the reasons driving the movement. A variety of techniques including machine learning, natural language processing, and spatiotemporal analysis are adopted to realize effective detection. Based on one-year Twitter data collected in Toronto, 2014 Toronto International Film Festival and traffic anomaly detection are selected as two case studies to evaluate the performance of proposed approaches. Through comparing with the ground truth data, the result reveals that more than 80% of the detected events do refer to real-world events, which illustrates the feasibility and efficiency of proposed approaches.<br></div><div><br></div><div>Keywords: Small-scale event, Event detection, Geosocial media data, Traffic event, Social event, Twitter, Spatiotemporal clustering<br></div>

Typical event sequences as licensors of direct object ellipsis in Russian

2018 ◽  
Vol 41 (2) ◽  
pp. 179-212
Author(s):  
Marjorie McShane
Keyword(s):  
Intelligent Agents ◽  
Cognitive Modeling ◽  
Direct Object ◽  
Event Sequence ◽  
Language Understanding ◽  
Modeling Effort ◽  
Event Sequences ◽  
Sequence Of Events ◽  
Typical Sequence ◽  
Typical Event

Abstract This paper extends the computationally-oriented theory of ellipsis presented in McShane’s A Theory of Ellipsis (2005) by introducing the feature typical event sequence. It is argued that, in Russian, the presence of a typical sequence of events in a pair of clauses can be the key feature licensing the ellipsis of the latter’s direct object. The linguistic analysis contributes to a larger cognitive modeling effort aimed at configuring language-endowed intelligent agents with human-level language understanding capabilities.

A Methodology for Developing Adaptive Fuzzy Cognitive Maps for Decision Support

2000 ◽  
Vol 4 (6) ◽  
pp. 403-407 ◽  
Author(s):  
M. Shamim Khan ◽  
◽  
Alex Chong ◽  
Tom Gedeon
Keyword(s):  
Decision Support ◽  
Dynamic System ◽  
Hebbian Learning ◽  
Real Life ◽  
Cognitive Maps ◽  
Causal Link ◽  
Fuzzy Cognitive Maps ◽  
Event Sequence ◽  
Event Sequences ◽  
Sequence Encoding

Differential Hebbian Learning (DHL) was proposed by Kosko as an unsupervised learning scheme for Fuzzy Cognitive Maps (FCMs). DHL can be used with a sequence of state vectors to adapt the causal link strengths of an FCM. However, it does not guarantee learning of the sequence by the FCM and no concrete procedures for the use of DHL has been developed. In this paper a formal methodology is proposed for using DHL in the development of FCMs in a decision support context. The four steps in the methodology are: (1) Creation of a crisp cognitive map; (2) Identification of event sequences for use in DHL; (3) Event sequence encoding using DHL; (4) Revision of the trained FCM. Feasibility of the proposed methodology is demonstrated with an example involving a dynamic system with feedback based on a real-life scenario.

Social Events

2013 ◽  
Vol 19 (S3) ◽  
pp. 6-6
Keyword(s):  
Social Event ◽  
Great Opportunity ◽  
Social Time ◽  
Social Events ◽  
Convention Center ◽  
Catch Up

This year's social event in the grand Indiana Convention Center Sagamore Ballroom will let you maximize your social time with your friends and colleagues without having to worry about the weather or get on a bus. The Sunday Social Event is a great opportunity to catch up with all your friends and colleagues, make some new ones, and enjoy some wine or local beer and a delicious supper buffet. It's all under one roof!

On Comparative History

1957 ◽  
Vol 9 (2) ◽  
pp. 267-279 ◽  
Author(s):  
Bert F. Hoselitz
Keyword(s):  
Social Sciences ◽  
Social Science ◽  
Comparative Method ◽  
A Priori ◽  
John Stuart Mill ◽  
Social Event ◽  
Social Scientists ◽  
Causal Factors ◽  
Social Events ◽  
The Social

When John Stuart Mill composed his System of Logic, he maintained that valid application of the comparative method to problems in the moral or social sciences is impossible, or, at best, inadmissible, since it must be based on a priori judgments. Mill founded his objection to the use of this method in social science on two essentially interrelated propositions: the uniqueness of each social event, and the multiplicity and variety of causal factors which may be considered as having a determining influence on these events. Although this conception of the special nature of social events has, on the whole, remained unchanged, social scientists have freely applied the comparative method to the analysis of social problems. History has been outstanding among the social sciences in rejecting longest the application of this method. The main argument against its use was derived from the description of history formulated by Ranke and his school, a description which was endowed with a philosophical underpinning by Windelband and Rickert, who classified sciences according to method into a nomothetic and an ideographic group. History was the ideographic science par excellence, and with the strong historical emphasis that was placed in Germany upon other social sciences as well, there was a tendency to return to the viewpoint of Mill and to regard as scientifically suspect generalizations in social science based on the application of the comparative method.

scholarly journals Streaming Social Event Detection and Evolution Discovery in Heterogeneous Information Networks

2021 ◽  
Vol 15 (5) ◽  
pp. 1-33
Author(s):  
Hao Peng ◽  
Jianxin Li ◽  
Yangqiu Song ◽  
Renyu Yang ◽  
Rajiv Ranjan ◽  
...  
Keyword(s):  
Real Time ◽  
Real World ◽  
Event Detection ◽  
Social Event ◽  
Heterogeneous Information ◽  
Heterogeneous Information Networks ◽  
Social Events ◽  
Meta Path ◽  
Social Event Detection ◽  
Event Based

Events are happening in real world and real time, which can be planned and organized for occasions, such as social gatherings, festival celebrations, influential meetings, or sports activities. Social media platforms generate a lot of real-time text information regarding public events with different topics. However, mining social events is challenging because events typically exhibit heterogeneous texture and metadata are often ambiguous. In this article, we first design a novel event-based meta-schema to characterize the semantic relatedness of social events and then build an event-based heterogeneous information network (HIN) integrating information from external knowledge base. Second, we propose a novel Pairwise Popularity Graph Convolutional Network, named as PP-GCN, based on weighted meta-path instance similarity and textual semantic representation as inputs, to perform fine-grained social event categorization and learn the optimal weights of meta-paths in different tasks. Third, we propose a streaming social event detection and evolution discovery framework for HINs based on meta-path similarity search, historical information about meta-paths, and heterogeneous DBSCAN clustering method. Comprehensive experiments on real-world streaming social text data are conducted to compare various social event detection and evolution discovery algorithms. Experimental results demonstrate that our proposed framework outperforms other alternative social event detection and evolution discovery techniques.

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