A fuzzy logic-based text classification method for social media

Social media offer abundant information for studying people’s behaviors, emotions and opinions during the evolution of various rare events such as natural disasters. It is useful to analyze the correlation between social media and human-affected events. This study uses Hurricane Sandy 2012 related Twitter text data to conduct information extraction and text classification. Considering that the original data contains different topics, we need to find the data related to Hurricane Sandy. A fuzzy logic-based approach is introduced to solve the problem of text classification. Inputs used in the proposed fuzzy logic-based model are multiple useful features extracted from each Twitter’s message. The output is its degree of relevance for each message to Sandy. A number of fuzzy rules are designed and different defuzzification methods are combined in order to obtain desired classification results. This work compares the proposed method with the well-known keyword search method in terms of correctness rate and quantity. The result shows that the proposed fuzzy logic-based approach is more suitable to classify Twitter messages than keyword word method

Let Them Tweet Cake: Estimating Public Dissent Using Twitter

This paper establishes a new method of estimating public dissent that is both cost-effective and adaptable. Twitter allows users to post short messages that can be viewed and shared by other users, creating a network of freely and easily observable information. Drawing data directly from Twitter, we collect tweets containing specified words and phrases from citizens voicing dissatisfaction with their government. The collected tweets are processed using a regular expression based algorithm to estimate individual dissent; which is aggregated to an overall measure of public dissent. A comparative case study of Canada and Kenya during the summer of 2016 provides proof of concept. Controlling for user base differences, we find there is more public dissent in Kenya than Canada. This obvious, but necessary, result suggests that our measure of public dissent is a better representation of each country’s internal dynamics than other more sporadic measures. As a robustness check, we test our estimates against real-world civil unrest events. Results show our estimates of public dissent are significantly predictive of civil unrest events days before they occur in both countries

Predictive Analysis on Twitter: Techniques and Applications

Predictive analysis of social media data has attracted considerable attention from the research community as well as the business world because of the essential and actionable information it can provide. Over the years, extensive experimentation and analysis for insights have been carried out using Twitter data in various domains such as healthcare, public health, politics, social sciences, and demographics. In this chapter, we discuss techniques, approaches and state-of-the-art applications of predictive analysis of Twitter data. Specifically, we present fine-grained analysis involving aspects such as sentiment, emotion, and the use of domain knowledge in the coarse-grained analysis of Twitter data for making decisions and taking actions, and relate a few success stories

Attention and counter-framing in the Black Lives Matter movement on Twitter

The social media platform Twitter platform has played a crucial role in the Black Lives Matter (BLM) movement. The immediate, flexible nature of tweets plays a crucial role both in spreading information about the movement’s aims and in organizing individual protests. Twitter has also played an important role in the right-wing reaction to BLM, providing a means to reframe and recontextualize activists’ claims in a more sinister light. The ability to bring about social change depends on the balance of these two forces, and in particular which side can capture and maintain sustained attention. The present study examines 2 years worth of tweets about BLM (about 118 million in total). Timeseries analysis reveals that activists are better at mobilizing rapid attention, whereas right-wing accounts show a pattern of moderate but more sustained activity driven by reaction to political opponents. Topic modeling reveals differences in how different political groups talk about BLM. Most notably, the murder of George Floyd appears to have solidified a right-wing counter-framing of protests as arising from dangerous “terrorist” actors. The study thus sheds light on the complex network and rhetorical effects that drive the struggle for online attention to the BLM movement

Analyzing evolution of rare events through social media data

Recently, some researchers have attempted to find a relationship between the evolution of rare events and temporal-spatial patterns of social media activities. Their studies verify that the relationship exists in both time and spatial domains. However, few of those studies can accurately deduce a time point when social media activities are most highly affected by a rare event because producing an accurate temporal pattern of social media during the evolution of a rare event is very difficult. This work expands the current studies along three directions. Firstly, we focus on the intensity of information volume and propose an innovative clustering algorithm-based data processing method to characterize the evolution of a rare event by analyzing social media data. Secondly, novel feature extraction and fuzzy logic-based classification methods are proposed to distinguish and classify event-related and unrelated messages. Lastly, since many messages do not have ground truth, we execute four existing ground-truth inference algorithms to deduce the ground truth and compare their performances. Then, an Adaptive Majority Voting (Adaptive MV) method is proposed and compared with two of the existing algorithms based on a set containing manually-labeled social media data. Our case studies focus on Hurricane Sandy in 2012 and Hurricane Maria in 2017. Twitter data collected around them are used to verify the effectiveness of the proposed methods. Firstly, the results of the proposed data processing method not only verify that a rare event and social media activities have strong correlations, but also reveal that they have some time difference. Thus, it is conducive to investigate the temporal pattern of social media activities. Secondly, fuzzy logic-based feature extraction and classification methods are effective in identifying event-related and unrelated messages. Lastly, the Adaptive MV method deduces the ground truth well and performs better on datasets with noisy labels than other two methods, Positive Label Frequency Threshold and Majority Voting

Analysis of Social Unrest Events using Spatio-Temporal Data Clustering and Agent-Based Modelling

Social unrest such as appeals, protests, conflicts, fights and mass violence can result from a wide ranging of diverse factors making the analysis of causal relationships challenging, with high complexity and uncertainty. Unrest events can result in significant changes in a society ranging from new policies and regulations to regime change. Widespread unrest often arises through a process of feedback and cascading of a collection of past events over time, in regions that are close to each other. Understanding the dynamics of these social events and extrapolating their future growth will enable analysts to detect or forecast major societal events. The study and prediction of social unrest has primarily been done through case-studies and study of social media messaging using various natural language processing techniques. The grouping of related events is often done by subject matter experts that create profiles for countries or locations. We propose two approaches in understanding and modelling social unrest data: (1) spatio-temporal data clustering, and (2) agent-based modelling. We apply the clustering solution to real-world unrest events with socioeconomic and infrastructure factors. We also present a framework of an agent-based model where unrest events act as intelligent agents that continuously study their environment and perform actions. We run simulations of the agent-based model under varying conditions and evaluate the results in comparison to real-world data. Our results show the viability of our proposed solutions. Adviser: Leen-Kiat Soh and Ashok Sama

An online framework for civil unrest prediction using tweet stream based on tweet weight and event diffusion

Twitter is one of most popular Internet-based social networking platform to share feelings, views, and opinions. In recent years, many researchers have utilized the social dynamic property of posted messages or tweets to predict civil unrest in advance. However, existing frameworks fail to describe the low granularity level of tweets and how they work in offline mode. Moreover, most of them do not deal with cases where enough tweet information is not available. To overcome these limitations, this article proposes an online framework for analyzing tweet stream inpredicting future civil unrest events. The framework filters tweet stream and classifies tweets using linear Support Vector Machine (SVM) classifier. After that, the weight of the tweet is measured and distributed among extracted locations to update the overall weight in each location in a day in a fully online manner. The weight history is then used to predict the status of civil unrest in a location. The significant contributions of this article are (i) A new keyword dictionary with keyword score to quantify sentiment in extracting the low granularity level of knowledge (ii) A new diffusion model for extracting locations of interest and distributing the sentiment among the locations utilizing the concept of information diffusion and location graph to handle locations with insufficient information (iii) Estimating the probability of civil unrest and determining the stages of unrest in upcoming days. The performance of the proposed framework has been measured and compared with existing logistic regression based predictive framework. The results showed that the proposed framework outperformed the existing framework in terms of F1 score, accuracy, balanced accuracy, false acceptance rate, false rejection rate, and Matthews correlation coefficient