Organized Behavior Classification of Tweet Sets using Supervised Learning Methods

During the 2016 US elections Twitter experienced unprecedented levels of propaganda and fake news through the collaboration of bots and hired persons, the ramifications of which are still being debated. This work proposes an approach to identify the presence of organized behavior in tweets. The Random Forest, Support Vector Machine, and Logistic Regression algorithms are each used to train a model with a data set of 850 records consisting of 299 features extracted from tweets gathered during the 2016 US presidential election. The features represent user and temporal synchronization characteristics to capture coordinated behavior. These models are trained to classify tweet sets among the categories: organic vs organized, political vs non-political, and pro-Trump vs pro-Hillary vs neither. The random forest algorithm performs better with greater than 95% average accuracy and f-measure scores for each category. The most valuable features for classification are identified as user based features, with media use and marking tweets as favorite to be the most dominant.Comment: 51 pages, 5 figure

An Investigation of Predictors of Information Diffusion in Social Media: Evidence from Sentiment Mining of Twitter Messages

Social media have facilitated information sharing in social networks. Previous research shows that sentiment of text influences its diffusion in social media. Each emotion can be located on a three-dimensional space formed by dimensions of valence (positive–negative), arousal (passive / calm–active / excited), and tension (tense–relaxed). While previous research has investigated the effect of emotional valence on information diffusion in social media, the effect of emotional arousal remains unexplored. This study examines how emotional arousal influences information diffusion in social media using a sentiment mining approach. We propose a research model and test it using data collected from Twitter

Design and Evaluation of Crowd-sourcing Platforms Based on Users Confidence Judgments

Crowd-sourcing deals with solving problems by assigning them to a large number of non-experts called crowd using their spare time. In these systems, the final answer to the question is determined by summing up the votes obtained from the community. The popularity of using these systems has increased by facilitation of access to community members through mobile phones and the Internet. One of the issues raised in crowd-sourcing is how to choose people and how to collect answers. Usually, the separation of users is done based on their performance in a pre-test. Designing the pre-test for performance calculation is challenging; The pre-test questions should be chosen in a way that they test the characteristics in people related to the main questions. One of the ways to increase the accuracy of crowd-sourcing systems is to pay attention to people's cognitive characteristics and decision-making model to form a crowd and improve the estimation of the accuracy of their answers to questions. People can estimate the correctness of their responses while making a decision. The accuracy of this estimate is determined by a quantity called metacognition ability. Metacoginition is referred to the case where the confidence level is considered along with the answer to increase the accuracy of the solution. In this paper, by both mathematical and experimental analysis, we would answer the following question: Is it possible to improve the performance of the crowd-sourcing system by knowing the metacognition of individuals and recording and using the users' confidence in their answers

TAPESTRY:A Blockchain based Service for Trusted Interaction Online

We present a novel blockchain based service for proving the provenance of online digital identity, exposed as an assistive tool to help non-expert users make better decisions about whom to trust online. Our service harnesses the digital personhood (DP); the longitudinal and multi-modal signals created through users' lifelong digital interactions, as a basis for evidencing the provenance of identity. We describe how users may exchange trust evidence derived from their DP, in a granular and privacy-preserving manner, with other users in order to demonstrate coherence and longevity in their behaviour online. This is enabled through a novel secure infrastructure combining hybrid on- and off-chain storage combined with deep learning for DP analytics and visualization. We show how our tools enable users to make more effective decisions on whether to trust unknown third parties online, and also to spot behavioural deviations in their own social media footprints indicative of account hijacking.Comment: Submitted to IEEE TSC Special Issue on Blockchain Services, May 201

Examining Polarized COVID-19 Twitter Discussion Using Inverse Reinforcement Learning

In this work, we model users\u27 behavior on Twitter in discussion of the COVID-19 outbreak using inverse reinforcement learning to better understand the underlying forces that drive the observed pattern of polarization. In doing so, we address the largely untapped potential of inverse reinforcement learning to model users\u27 behavior on social media, and contribute to the body of sociology, psychology, and communication research seeking to elucidate the causes of socio-cultural polarization. We hypothesize that structural characteristics of each week\u27s retweet network as well as COVID-19 data on cases, hospitalizations, and outcomes are related to the Twitter users\u27 reward function which leads to polarized discussion of COVID-19 on the platform. To derive the state space of our inverse reinforcement learning model, we compute the relative modularity of retweet networks formed from retweets about COVID-19. The action space is determined by the distribution of mask-wearing sentiment in tweets about COVID-19. We build a fine-tune a BERT text classifier to determine mask-wearing sentiment in tweet. We design state features which reflect both structural characteristics of the retweet networks and COVID-19 data on cases, hospitalizations, and outcomes. Our results indicate that polarized Twitter discussion about COVID-19 weighs more heavily on features relating to the severity of the COVID-19 outbreak and less heavily on features relating to the structure of retweet networks. Overall, our results demonstrate the aptitude of inverse reinforcement learning in helping understand user behavior on social media

Sentiment analysis and classification of Indian farmers’ protest using twitter data

Protests are an integral part of democracy and an important source for citizens to convey their demands and/or dissatisfaction to the government. As citizens become more aware of their rights, there has been an increasing number of protests all over the world for various reasons. With the advancement of technology, there has also been an exponential rise in the use of social media to exchange information and ideas. In this research, we gathered data from the microblogging website Twitter concerning farmers’ protest to understand the sentiments that the public shared on an international level. We used models to categorize and analyze the sentiments based on a collection of around 20,000 tweets on the protest. We conducted our analysis using Bag of Words and TF-IDF and discovered that Bag of Words performed better than TF-IDF. In addition, we also used Naive Bayes, Decision Trees, Random Forests, and Support Vector Machines and also discovered that Random Forest had the highest classification accuracy

Effects Of Interdisciplinary Designers Reflecting-In-Action During Design

As a specific type of reflective practice, reflection-in-action emphasizes that unique and uncertain situations are understood through attempts to change them, and changed through the attempts to understand the situations (Schön, 1983). The purpose of this interdisciplinary research was to study reflection-in-action regarding three aspects of design activity (content, context, and process). The study addressed four research questions: (a) what is the impact of reflection-in-action on evaluation processes while a design is developing and not yet complete, (b) what effect does reflection-in-action have on keeping a design project moving forward toward implementation, (c) what impact does the design\u27s problem-solution relationship have on the reflection-in-action process, and (d) what impact does a designer drawing from a repertoire of precedents inside and outside the project have on the reflection-in-action process? The phenomenological research design studied reflection-in-action using a qualitative approach and used a purposive convenience sample of eight participants designing real projects in their design environments. Using five data collection methods: (a) interviews (b) participant reflective journals, (c) design project timeline, (d) project artifact analysis, and (e) a field journal, data were collected and trustworthiness was established through credibility, transferability, dependability, and confirmability. A constant comparison method was used to compare information units applicable to categories and to integrate properties of categories. For each research question, three to five themes emerged. Interesting and compelling themes that have implications for instructional design included when participants reflected-in-action, they took stock in and reacted to external representations, which were rich in context, information, and constraints. Participants interacted with information and a lack of information, which kept the design project moving forward. Participants moved the design forward toward implementation by turning what ifs to design decisions. Through receiving and gathering information and working with constraints, participants better understood the problem-solution relationship. Drawing from outside of the design validated design direction, guided the design, and provided what ifs . Drawing from inside the design informed what could and could not be done, supported the design purpose, and guided the design. Drawing on participants\u27 experience provided design context and made uncertainty more certain

Voice and silence in public debate: Modelling and observing collective opinion expression online

This thesis investigates how group-level differences in willingness of opinion expression shape the extent to which certain standpoints are visible in public debate online. Against the backdrop of facilitated communication and connection to like-minded others through digital technologies, models and methods are developed and case studies are carried out – by and large from a network perspective. To this end, we first propose a model of opinion dynamics that examines social- structural conditions for public opinion expression or even predominance of different groups. The model focuses not on opinion change, but on the decision of individuals whether to express their opinion publicly or not. Groups of agents with different, fixed opinions interact with each other, changing the willingness to express their opinion according to the feedback they receive from others. We formulate the model as a multi-group game, and subsequently provide a dynamical systems perspective by introducing reinforcement learning dynamics. We show that a minority can dominate public discourse if its internal connections are sufficiently dense. Moreover, increased costs for opinion expression can drive even internally well-connected groups into silence. We then focus on how interaction networks can be used to infer political and social positions. For this purpose, we develop a new type of force-directed network layout algorithm. While being widely used, a rigorous interpretation of the outcomes of existing force-directed algorithms has not been provided yet. We argue that interpretability can be delivered by latent space approaches, which have the goal of embedding a network in an underlying social space. On the basis of such a latent space model, we derive a force-directed layout algorithm that can not only be used for the spatialisation of generic network data – exemplified by Twitter follower and retweet networks, as well as Facebook friendship networks – but also for the visualization of surveys. Comparison to existing layout algorithms (which are not grounded in an interpretable model) reveals that node groups are placed in similar configurations, while said algorithms show a stronger intra-cluster separation of nodes, as well as a tendency to separate clusters more strongly in retweet networks. In two case studies, we observe actual public debate on the social media platform Twitter – topics are the Saxon state elections 2019, and violent riots in the city of Leipzig on New Year’s Eve in the same year. We show that through the interplay of retweet and reply networks, it is possible to identify differences in willingness of opinion expression on the platform between opinion groups. We find that for both events, propensities to get involved in debate are asymmetric. Users retweeting far-right parties and politicians are significantly more active, making their positions disproportionately visible. Said users also act significantly more confrontational in the sense that they reply mostly to users from different groups, while the contrary is not the case. The findings underline that naive reliance on what others express online can be collectively dangerous, especially in an era in which social media shapes public discourse to an unprecedented extent

VaxInsight: an artificial intelligence system to access large-scale public perceptions of vaccination from social media

Vaccination is considered one of the greatest public health achievements of the 20th century. A high vaccination rate is required to reduce the prevalence and incidence of vaccine-preventable diseases. However, in the last two decades, there has been a significant and increasing number of people who refuse or delay getting vaccinated and who prohibit their children from receiving vaccinations. Importantly, under-vaccination is associated with infectious disease outbreaks. A good understanding of public perceptions regarding vaccinations is important if we are to develop effective vaccination promotion strategies. Traditional methods of research, such as surveys, suffer limitations that impede our understanding of public perceptions, including resources cost, delays in data collection and analysis, especially in large samples. The popularity of social media (e.g. Twitter), combined with advances in artificial intelligence algorithms (e.g. natural language processing, deep learning), open up new avenues for accessing large scale data on public perceptions related to vaccinations. This dissertation reports on an original and systematic effort to develop artificial intelligence algorithms that will increase our ability to use Twitter discussions to understand vaccine-related perceptions and intentions. The research is framed within the perspectives offered by grounded behavior change theories. Tweets concerning the human papillomavirus (HPV) vaccine were used to accomplish three major aims: 1) Develop a deep learning-based system to better understand public perceptions of the HPV vaccine, using Twitter data and behavior change theories; 2) Develop a deep learning-based system to infer Twitter users’ demographic characteristics (e.g. gender and home location) and investigate demographic differences in public perceptions of the HPV vaccine; 3) Develop a web-based interactive visualization system to monitor real-time Twitter discussions of the HPV vaccine. For Aim 1, the bi-directional long short-term memory (LSTM) network with attention mechanism outperformed traditional machine learning and competitive deep learning algorithms in mapping Twitter discussions to the theoretical constructs of behavior change theories. Domain-specific embedding trained on HPV vaccine-related Twitter corpus by fastText algorithms further improved performance on some tasks. Time series analyses revealed evolving trends of public perceptions regarding the HPV vaccine. For Aim 2, the character-based convolutional neural network model achieved favorable state-of-the-art performance in Twitter gender inference on a Public Author Profiling challenge. The trained models then were applied to the Twitter corpus and they identified gender differences in public perceptions of the HPV vaccine. The findings on gender differences were largely consistent with previous survey-based studies. For the Twitter users’ home location inference, geo-tagging was framed as text classification tasks that resulted in a character-based recurrent neural network model. The model outperformed machine learning and deep learning baselines on home location tagging. Interstate variations in public perceptions of the HPV vaccine also were identified. For Aim 3, a prototype web-based interactive dashboard, VaxInsight, was built to synthesize HPV vaccine-related Twitter discussions in a comprehendible format. The usability test of VaxInsight showed high usability of the system. Notably, this maybe the first study to use deep learning algorithms to understand Twitter discussions of the HPV vaccine within the perspective of grounded behavior change theories. VaxInsight is also the first system that allows users to explore public health beliefs of vaccine related topics from Twitter. Thus, the present research makes original and systematical contributions to medical informatics by combining cutting-edge artificial intelligence algorithms and grounded behavior change theories. This work also builds a foundation for the next generation of real-time public health surveillance and research

Mining Behavior of Citizen Sensor Communities to Improve Cooperation with Organizational Actors

Web 2.0 (social media) provides a natural platform for dynamic emergence of citizen (as) sensor communities, where the citizens generate content for sharing information and engaging in discussions. Such a citizen sensor community (CSC) has stated or implied goals that are helpful in the work of formal organizations, such as an emergency management unit, for prioritizing their response needs. This research addresses questions related to design of a cooperative system of organizations and citizens in CSC. Prior research by social scientists in a limited offline and online environment has provided a foundation for research on cooperative behavior challenges, including \u27articulation\u27 and \u27awareness\u27, but Web 2.0 supported CSC offers new challenges as well as opportunities. A CSC presents information overload for the organizational actors, especially in finding reliable information providers (for awareness), and finding actionable information from the data generated by citizens (for articulation). Also, we note three data level challenges: ambiguity in interpreting unconstrained natural language text, sparsity of user behaviors, and diversity of user demographics. Interdisciplinary research involving social and computer sciences is essential to address these socio-technical issues. I present a novel web information-processing framework, called the Identify-Match- Engage (IME) framework. IME allows operationalizing computation in design problems of awareness and articulation of the cooperative system between citizens and organizations, by addressing data problems of group engagement modeling and intent mining. The IME framework includes: a.) Identification of cooperation-assistive intent (seeking-offering) from short, unstructured messages using a classification model with declarative, social and contrast pattern knowledge, b.) Facilitation of coordination modeling using bipartite matching of complementary intent (seeking-offering), and c.) Identification of user groups to prioritize for engagement by defining a content-driven measure of \u27group discussion divergence\u27. The use of prior knowledge and interplay of features of users, content, and network structures efficiently captures context for computing cooperation-assistive behavior (intent and engagement) from unstructured social data in the online socio-technical systems. Our evaluation of a use-case of the crisis response domain shows improvement in performance for both intent classification and group engagement prioritization. Real world applications of this work include use of the engagement interface tool during various recent crises including the 2014 Jammu and Kashmir floods, and intent classification as a service integrated by the crisis mapping pioneer Ushahidi\u27s CrisisNET project for broader impact