scholarly journals Describing Locations Using Tags and Images: Explorative Pattern Mining in Social Media

2012 ◽  
pp. 77-96 ◽  
Author(s):  
Florian Lemmerich ◽  
Martin Atzmueller
Keyword(s):  
Social Media ◽  
Pattern Mining

Short Semantic Patterns: A Linguistic Pattern Mining Approach for Content Analysis Applied to Hate Speech

2020 ◽  
Vol 29 (02) ◽  
pp. 2040002
Author(s):  
Danielly Sorato ◽  
Fábio B. Goularte ◽  
Renato Fileto
Keyword(s):  
Social Media ◽  
Content Analysis ◽  
Pattern Mining ◽  
Hate Speech ◽  
Binary Classification ◽  
Word Embedding ◽  
Linguistic Patterns ◽  
Islamic Religion

Microblog posts such as tweets frequently contain users’ opinions and thoughts about events, products, people, institutions, etc. However, the usage of social media to prop-agate hate speech is not an uncommon occurrence. Analyzing hateful speech in social media is essential for understanding, fighting and discouraging such actions. We believe that by extracting fragments of text that are semantically similar it is possible to depict recurrent linguistic patterns in certain kinds of discourse. Therefore, we aim to use these patterns to encapsulate frequent statements textually expressed in microblog posts. In this paper, we propose to exploit such linguistic patterns in the context of hate speech. Through a technique that we call SSP (Short Semantic Pattern) mining, we are able to extract sequences of words that share a similar meaning in their word embedding representation. By analyzing the extracted patterns, we reveal some kinds of discourses that are replayed across a dataset, such as racist and sexist statements. Afterwards, we experiment using SSP as features to build classifiers that detect if a tweet contains hate speech (binary classification) and to distinguish between sexist, racist and clean tweets (ternary classification). The SSP instances encountered in tweets containing sexism have shown that a large number of sexist tweets began with the introduction ‘I’m not sexist but’ and ‘Call me sexist but’. Meanwhile, SSP instances found in tweets reproducing racism revealed a prominence of contents against the Islamic religion, associated entities and organizations.

scholarly journals ANALYSIS ON COGNITIVE SOCIAL DATA FUSION

2020 ◽  
pp. 27-30
Author(s):  
Anandakumar H ◽  
Tamilselvan T ◽  
Nandni S ◽  
Subashree R ◽  
Vinodhini E
Keyword(s):  
Social Media ◽  
Big Data ◽  
Data Fusion ◽  
Data Processing ◽  
Pattern Mining ◽  
Data Representation ◽  
Map Reduce ◽  
User Interest ◽  
Social Data ◽  
Algorithm Mapping

Big data stands for effective handling of large amount of data, research, mining, intelligence. In social media large amount of data uploaded every.Social media handle large amount of data like photo, video, songs and so many using big data. When it comes for big data, a large amount of data should be effectively handled. Big data face various challenges like clustering of data, visualizing, data representation, data processing, pattern mining, tracking of data and analysing behaviour of users. In this paper the Emoji in messages are decoded and Unicode will be set. Based on the Emoji the user interest can be understood in a better way. Then another part involves the replacement of repeated data by using the map Reduce algorithm. Mapping of data with key values used to reduce the size of storage.

Trajectory Pattern Mining: Methods and Applications

2014 ◽  
Vol 490-491 ◽  
pp. 1361-1367
Author(s):  
Xin Huang ◽  
Hui Juan Chen ◽  
Mao Gong Zheng ◽  
Ping Liu ◽  
Jing Qian
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Pattern Mining ◽  
Data Sets ◽  
Trajectory Data ◽  
Data Mining Algorithms ◽  
Mining Methods ◽  
Trajectory Pattern ◽  
Mining Algorithms ◽  
Trajectory Pattern Mining

With the advent of location-based social media and locationacquisition technologies, trajectory data are becoming more and more ubiquitous in the real world. A lot of data mining algorithms have been successfully applied to trajectory data sets. Trajectory pattern mining has received a lot of attention in recent years. In this paper, we review the most inuential methods as well as typical applications within the context of trajectory pattern mining.

scholarly journals Tracing the emotional roadmap of depressive users on social media through sequential pattern mining

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Felipe Taliar Giuntini ◽  
Kaue L. De Moraes ◽  
Mirela T. Cazzolato ◽  
Luziane de F. Kirchner ◽  
Maria de Jesus D. Dos Reis ◽  
...  
Keyword(s):  
Social Media ◽  
Pattern Mining ◽  
Sequential Pattern Mining ◽  
Sequential Pattern

scholarly journals Activity Pattern Mining from Social Media for Healthcare Monitoring on Big data

2019 ◽  
Vol 8 (9S3) ◽  
pp. 527-530
Keyword(s):  
Social Media ◽  
Big Data ◽  
Activity Pattern ◽  
Pattern Mining ◽  
Medical Practitioner ◽  
Time Data ◽  
Community Healthcare ◽  
Big Data Applications ◽  
Technological Developments ◽  
Analytical Tools

Big data applications introduce novel openings for establishinginnovative information and produce differentadvanced methods to improve the worth of healthcare.In this paper, a novel activity pattern mining from social media for healthcare to examine big data applications in different biomedical multi-disciplines such as bioinformatics, medical imaging and community healthcare applications.Big data analytical tools perform the key part in their task for extracting hidden behavioural and expressive patterns frompersonal messages and their tweets. The behavioural patterns of the users can realizetheir additional informations about their concealed feelings and sentiments[1],[ 3],[5]. Further, the neural network is modelled to predict the psychological informations, such as nervousness, depression, behavioural disorder and mental stress.This is also shows that integrating variety of sources of data enables medical practitioner to show a novel investigation of patient care processes, improvements in new mobile healthcare technological developments aid real-time data collection, archiving and analysis of data in distributed environments

scholarly journals Analysis of Instagram Users’ Movement Pattern by Cluster Analysis and Association Rule Mining

2022 ◽  
pp. 97-109
Author(s):  
Zehui Wang ◽  
Luca Koroll ◽  
Wolfram Höpken ◽  
Matthias Fuchs
Keyword(s):  
Social Media ◽  
Pattern Mining ◽  
Movement Pattern ◽  
Noise Removal ◽  
Rule Mining ◽  
Social Media Data ◽  
Social Media Platforms ◽  
Information And Communication ◽  
Data Source ◽  
Media Data

AbstractUnderstanding the characteristics of tourists’ movements is essential for tourism destination management. With advances in information and communication technology, more and more people are willing to upload photos and videos to various social media platforms while traveling. These openly available media data is gaining increasing attention in the field of movement pattern mining as a new data source. In this study, uploaded images and their geographic information within Lake Constance region, Germany were collected and through clustering analysis, a state-of-the-art k-means with noise removal algorithm was compared with the commonly used DBCSCAN on Instagram dataset. Finally, association rules between popular attractions at region-level and city-level were mined respectively. Results show that social media data like Instagram constitute a valuable input to analyse tourists’ movement patterns as input to decision support and destination management.

scholarly journals Pattern Mining Approaches Used in Social Media Data

2020 ◽  
Vol 28 (Supp02) ◽  
pp. 123-152
Author(s):  
Jyotismita Chaki ◽  
Nilanjan Dey ◽  
B. K. Panigrahi ◽  
Fuqian Shi ◽  
Simon James Fong ◽  
...  
Keyword(s):  
Social Media ◽  
Pattern Mining ◽  
Strong Dependence ◽  
Future Research ◽  
Data Generation ◽  
Grammatical Structure ◽  
Social Media Data ◽  
Social Media Networks ◽  
Research Guidelines ◽  
Media Data

Social media conveys a reachable platform for users to share information. The inescapable practice of social media has produced remarkable volumes of social data. Social media gathers the data in both structured-unstructured and formal-informal ways as users are not concerned with the exact grammatical structure and spelling when interacting with each other by means of various social networking websites (Twitter, Facebook, YouTube, LinkedIn, etc.). People are increasingly involved in and dependent on social media networks for data, news and opinions of other handlers on a variety of topics. The strong dependence on social media network sites contributes to enormous data generation characterized by three issues: scale, noise, and variety. Such problems also hinder social network data to be evaluated manually, resulting in the correct use of statistical analytical methods. Mining social media data can extract significant patterns that can be advantageous for consumers, users, and business. Pattern mining offers a wide variety of methods to detect valuable knowledge from huge datasets, such as patterns, trends, and rules. In this work, data was collected comprised of users’ opinions and sentiments and then processed using a significant number of pattern mining methods. The results were then further analyzed to attain meaningful information. The aim of this paper is to deliver a summary and a set of strategies for utilizing the ubiquitous pattern mining approaches, and to recognize the challenges and future research guidelines of dealing out social media data.

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