Local Event Detection Scheme by Analyzing Relevant Documents in Social Networks

Dojin Choi; Soobin Park; Dongho Ham; Hunjin Lim; Kyoungsoo Bok; Jaesoo Yoo

doi:10.3390/app11020577

Local Event Detection Scheme by Analyzing Relevant Documents in Social Networks

Applied Sciences ◽

10.3390/app11020577 ◽

2021 ◽

Vol 11 (2) ◽

pp. 577

Author(s):

Dojin Choi ◽

Soobin Park ◽

Dongho Ham ◽

Hunjin Lim ◽

Kyoungsoo Bok ◽

...

Keyword(s):

Social Networks ◽

Event Detection ◽

Performance Evaluations ◽

Data Dictionary ◽

Detection Scheme ◽

Geographical Data ◽

Network Characteristics ◽

Related Information ◽

Event Based ◽

Detection Schemes

In this paper, we propose a local event detection scheme by analyzing relevant documents in social networks to improve the accuracy of event detection. To detect local events by using geographical data, the proposed scheme embeds them using a geographical data dictionary and generates a weighted keyword graph using social network characteristics. The data left by users in social networks include not only postings but also related documents such as comments and threads. In this way, the proposed scheme detects a local event based on a keyword graph that is constructed through the analysis of the relevant documents. This can improve the accuracy of local event detection by analyzing relevant documents embedded with region-related information using a geographical data dictionary, without requiring users to tag geographic data. In order to verify the superiority of the proposed scheme, we compare it with the existing event detection schemes through various performance evaluations.

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Extracting Criminal-Related Events from Arabic Tweets

Journal of Information Technology Research ◽

10.4018/jitr.2017070103 ◽

2017 ◽

Vol 10 (3) ◽

pp. 34-47

Author(s):

Feriel Abdelkoui ◽

Mohamed-Khireddine Kholladi

Keyword(s):

Social Networks ◽

Natural Hazards ◽

Real World ◽

Event Detection ◽

Temporal Information ◽

Automated Extraction ◽

Temporal Expressions ◽

Spatio Temporal ◽

Event Based ◽

F Measure

Recently, Twitter as one of social networks has been considered as a rich source of spatio-temporal information and significant revenue for mining data. Event detection from tweets can help to predict more serious real-world events. Such as: criminal events, natural hazards, and the spread of epidemics. Etc. This paper deals with event-based extraction for criminal incidents from Arabic tweets. It presents a framework that supports automated extraction of spatial and temporal information from tweets. The proposed approach is based on combining various indicators, including the names of places and temporal expressions that appear in the tweet message, related tweeting time, and additional locations from the user's profile. The effectiveness of the system was evaluated in term of recall, precision and f-measure.

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HEXACO personality factors and their associations with Facebook use and Facebook network characteristics

10.31234/osf.io/3zvhq ◽

2018 ◽

Author(s):

Riana Brown ◽

Sam G. B. Roberts ◽

Thomas V. Pollet

Keyword(s):

Social Networks ◽

Network Size ◽

Openness To Experience ◽

Personality Factors ◽

Number Of Clusters ◽

Network Characteristics ◽

Online Networks ◽

Facebook Use ◽

Objectively Measured ◽

Size And Structure

Personality factors affect the properties of ‘offline’ social networks, but how they are associated with the structural properties of online networks is still unclear. We investigated how the six HEXACO personality factors (Honesty-Humility, Emotionality, Extraversion, Agreeableness, Conscientiousness and Openness to Experience) relate to Facebook use and three objectively measured Facebook network characteristics - network size, density, and number of clusters. Participants (n = 107, mean age = 20.6, 66% female) extracted their Facebook networks using the GetNet app, completed the 60-item HEXACO questionnaire and the Facebook Usage Questionnaire. Users high in Openness to Experience spent less time on Facebook. Extraversion was positively associated with network size and the number of network clusters (but not after controlling for size). These findings suggest that personality factors are associated with Facebook use and the size and structure of Facebook networks, and that personality is an important influence on both online and offline sociality.

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ARFIS: Adaptive-Receiver-Based Fuzzy Inference System for Diffusion- Based Molecular Communications

Current Nanoscience ◽

10.2174/1573413715666190625114949 ◽

2020 ◽

Vol 16 (2) ◽

pp. 280-289

Author(s):

Ghalib H. Alshammri ◽

Walid K. M. Ahmed ◽

Victor B. Lawrence

Keyword(s):

Fuzzy Inference System ◽

Fuzzy Inference ◽

Detection Threshold ◽

Learning Rule ◽

Optimal Number ◽

Molecular Communication ◽

Inference System ◽

Detection Scheme ◽

Adaptive Receiver ◽

Detection Schemes

Background: The architecture and sequential learning rule-based underlying ARFIS (adaptive-receiver-based fuzzy inference system) are proposed to estimate and predict the adaptive threshold-based detection scheme for diffusion-based molecular communication (DMC). Method: The proposed system forwards an estimate of the received bits based on the current molecular cumulative concentration, which is derived using sequential training-based principle with weight and bias and an input-output mapping based on both human knowledge in the form of fuzzy IFTHEN rules. The ARFIS architecture is employed to model nonlinear molecular communication to predict the received bits over time series. Result: This procedure is suitable for binary On-OFF-Keying (Book signaling), where the receiver bio-nanomachine (Rx Bio-NM) adapts the 1/0-bit detection threshold based on all previous received molecular cumulative concentrations to alleviate the inter-symbol interference (ISI) problem and reception noise. Conclusion: Theoretical and simulation results show the improvement in diffusion-based molecular throughput and the optimal number of molecules in transmission. Furthermore, the performance evaluation in various noisy channel sources shows promising improvement in the un-coded bit error rate (BER) compared with other threshold-based detection schemes in the literature.

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