scholarly journals InterSpot: Interactive Spammer Detection in Social Media

2019 ◽  
Author(s):  
Kaize Ding ◽  
Jundong Li ◽  
Shivam Dhar ◽  
Shreyash Devan ◽  
Huan Liu
Keyword(s):  
Social Media ◽  
Learning Process ◽  
Interactive Learning ◽  
Detection System ◽  
Feedback Information ◽  
Detection Model ◽  
Empirical Success ◽  
Detection Strategy ◽  
Current Detection ◽  
Over Time

Spammer detection in social media has recently received increasing attention due to the rocketing growth of user-generated data. Despite the empirical success of existing systems, spammers may continuously evolve over time to impersonate normal users while new types of spammers may also emerge to combat with the current detection system, leading to the fact that a built system will gradually lose its efficacy in spotting spammers. To address this issue, grounded on the contextual bandit model, we present a novel system for conducting interactive spammer detection. We demonstrate our system by showcasing the interactive learning process, which allows the detection model to keep optimizing its detection strategy through incorporating the feedback information from human experts.

scholarly journals SOCIAL MEDIA INTERDISCIPLINARITY IN MULTIMODALITY OF ARTS AND CULTURE EDUCATION FOR MILLENIALS

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Yasinda Widya Fahmi ◽  
Djuli Djatiprambudi ◽  
Warih Handayaningrum
Keyword(s):  
High School ◽  
Social Media ◽  
Junior High School ◽  
Learning Process ◽  
Junior High ◽  
Interactive Learning ◽  
Culture Learning ◽  
Problem Solving Skills ◽  
Arts And Culture ◽  
Art And Culture

This study aims to explore the problems of art and culture interactive learning at the Junior High School level which belongs to the millennial generation. The focus of the study lies in the interdisciplinary aspect of social media in its delivery in multimodality of arts and culture learning process. Furthermore, to find out about opportunities, challenges, and responses from students about the use of social media in its development as a medium in art and culture interactive learning. The research method uses qualitative-analytic. Data collection used observation techniques which were carried out from January 2020 to June 2020, and questionnaires to 75 art teachers and 500 Junior High School students who were taken randomly, with spatial boundaries in Surabaya, East Java. The results showed that the learning involvement experienced by students had complexity and multimodality, including collaborative work, observing and evaluating each other's work, and involvement in finding, identifying, and exploring trends related to delivery in social media as a medium for art and culture learning process. Furthermore, it's able to motivate students to be more actively involved in learning with a sense of joy; positioning artwork with others on social media; increase the contextual and conceptual understanding in the material of art and culture and apply it as a process of actualizing students' aesthetic skills; and improve critical thinking and problem-solving skills.

scholarly journals Impact of Unreliable Content on Social Media Users during COVID-19 and Stance Detection System

Electronics ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 5
Author(s):  
Mudasir Ahmad Wani ◽  
Nancy Agarwal ◽  
Patrick Bours
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Detection System ◽  
Online Social Network ◽  
Negative Attitude ◽  
Health Crisis ◽  
Online Groups ◽  
Detection Model ◽  
Twitter Users ◽  
The Impact

The abundant dissemination of misinformation regarding coronavirus disease 2019 (COVID-19) presents another unprecedented issue to the world, along with the health crisis. Online social network (OSN) platforms intensify this problem by allowing their users to easily distort and fabricate the information and disseminate it farther and rapidly. In this paper, we study the impact of misinformation associated with a religious inflection on the psychology and behavior of the OSN users. The article presents a detailed study to understand the reaction of social media users when exposed to unverified content related to the Islamic community during the COVID-19 lockdown period in India. The analysis was carried out on Twitter users where the data were collected using three scraping packages, Tweepy, Selenium, and Beautiful Soup, to cover more users affected by this misinformation. A labeled dataset is prepared where each tweet is assigned one of the four reaction polarities, namely, E (endorse), D (deny), Q (question), and N (neutral). Analysis of collected data was carried out in five phases where we investigate the engagement of E, D, Q, and N users, tone of the tweets, and the consequence upon repeated exposure of such information. The evidence demonstrates that the circulation of such content during the pandemic and lockdown phase had made people more vulnerable in perceiving the unreliable tweets as fact. It was also observed that people absorbed the negativity of the online content, which induced a feeling of hatred, anger, distress, and fear among them. People with similar mindset form online groups and express their negative attitude to other groups based on their opinions, indicating the strong signals of social unrest and public tensions in society. The paper also presents a deep learning-based stance detection model as one of the automated mechanisms for tracking the news on Twitter as being potentially false. Stance classifier aims to predict the attitude of a tweet towards a news headline and thereby assists in determining the veracity of news by monitoring the distribution of different reactions of the users towards it. The proposed model, employing deep learning (convolutional neural network(CNN)) and sentence embedding (bidirectional encoder representations from transformers(BERT)) techniques, outperforms the existing systems. The performance is evaluated on the benchmark SemEval stance dataset. Furthermore, a newly annotated dataset is prepared and released with this study to help the research of this domain.

scholarly journals SOCIAL MEDIA INTERDISCIPLINARITY IN MULTIMODALITY OF ARTS AND CULTURE EDUCATION FOR MILLENIALS

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Yasinda Widya Fahmi ◽  
Djuli Djatiprambudi ◽  
Warih Handayaningrum
Keyword(s):  
High School ◽  
Social Media ◽  
Junior High School ◽  
Learning Process ◽  
Junior High ◽  
Interactive Learning ◽  
Culture Learning ◽  
Problem Solving Skills ◽  
Arts And Culture ◽  
Art And Culture

This study aims to explore the problems of art and culture interactive learning at the Junior High School level which belongs to the millennial generation. The focus of the study lies in the interdisciplinary aspect of social media in its delivery in multimodality of arts and culture learning process. Furthermore, to find out about opportunities, challenges, and responses from students about the use of social media in its development as a medium in art and culture interactive learning. The research method uses qualitative-analytic. Data collection used observation techniques which were carried out from January 2020 to June 2020, and questionnaires to 75 art teachers and 500 Junior High School students who were taken randomly, with spatial boundaries in Surabaya, East Java. The results showed that the learning involvement experienced by students had complexity and multimodality, including collaborative work, observing and evaluating each other's work, and involvement in finding, identifying, and exploring trends related to delivery in social media as a medium for art and culture learning process. Furthermore, it's able to motivate students to be more actively involved in learning with a sense of joy; positioning artwork with others on social media; increase the contextual and conceptual understanding in the material of art and culture and apply it as a process of actualizing students' aesthetic skills; and improve critical thinking and problem-solving skills.

To Tweet or Not To Tweet?

2014 ◽  
Vol 43 (1) ◽  
pp. 40-41
Author(s):  
James LoRusso
Keyword(s):  
Social Media ◽  
Learning Process ◽  
New Technologies ◽  
Economic Context

This piece argues that new technologies generally, and social media in particular, are too often accepted uncritically and incorporated hastily into course designs for the humanities. The author encourages teachers to ask two basic questions when considering social media in the classroom: 1. Does social media actually improve the learning process? And 2. How are these technologies embedded in the larger socio-economic context?

Optimizing Error Rate in Intrusion Detection System Using Artificial Neural Network Algorithm

2018 ◽  
Vol 6 (9) ◽  
pp. 152
Author(s):  
S. Vijaya Rani ◽  
G. N. K. Suresh Babu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Intrusion Detection ◽  
Error Rate ◽  
Learning Process ◽  
Nearest Neighbor ◽  
Detection System ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Artificial Neural

The illegal hackers  penetrate the servers and networks of corporate and financial institutions to gain money and extract vital information. The hacking varies from one computing system to many system. They gain access by sending malicious packets in the network through virus, worms, Trojan horses etc. The hackers scan a network through various tools and collect information of network and host. Hence it is very much essential to detect the attacks as they enter into a network. The methods  available for intrusion detection are Naive Bayes, Decision tree, Support Vector Machine, K-Nearest Neighbor, Artificial Neural Networks. A neural network consists of processing units in complex manner and able to store information and make it functional for use. It acts like human brain and takes knowledge from the environment through training and learning process. Many algorithms are available for learning process This work carry out research on analysis of malicious packets and predicting the error rate in detection of injured packets through artificial neural network algorithms.

scholarly journals Real-time spatio-temporal event detection on geotagged social media

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Yasmeen George ◽  
Shanika Karunasekera ◽  
Aaron Harwood ◽  
Kwan Hui Lim
Keyword(s):  
New York ◽  
Social Media ◽  
Event Detection ◽  
Detection System ◽  
Time And Space ◽  
Social Media Data ◽  
Event Time ◽  
Spatio Temporal ◽  
Geographical Space ◽  
Media Data

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.

scholarly journals Echo chamber detection and analysis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Giacomo Villa ◽  
Gabriella Pasi ◽  
Marco Viviani
Keyword(s):  
Social Media ◽  
Community Detection ◽  
Virtual Communities ◽  
Selective Exposure ◽  
Social Needs ◽  
News Sources ◽  
Detection Strategy ◽  
Echo Chambers ◽  
Echo Chamber ◽  
Content Aware

AbstractSocial media allow to fulfill perceived social needs such as connecting with friends or other individuals with similar interests into virtual communities; they have also become essential as news sources, microblogging platforms, in particular, in a variety of contexts including that of health. However, due to the homophily property and selective exposure to information, social media have the tendency to create distinct groups of individuals whose ideas are highly polarized around certain topics. In these groups, a.k.a. echo chambers, people only "hear their own voice,” and divergent visions are no longer taken into account. This article focuses on the study of the echo chamber phenomenon in the context of the COVID-19 pandemic, by considering both the relationships connecting individuals and semantic aspects related to the content they share over Twitter. To this aim, we propose an approach based on the application of a community detection strategy to distinct topology- and content-aware representations of the COVID-19 conversation graph. Then, we assess and analyze the controversy and homogeneity among the different polarized groups obtained. The evaluations of the approach are carried out on a dataset of tweets related to COVID-19 collected between January and March 2020.

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