Characterising the Social Media Temporal Response to External Events

In recent years social media has become a crucial component of online information propagation. It is one of the fastest responding mediums to offline events, significantly faster than traditional news services. Popular social media posts can spread rapidly through the internet, potentially spreading misinformation and affecting human beliefs and behaviour. The nature of how social media responds allows inference about events themselves and provides insight into human behavioural characteristics. However, despite its importance, researchers don’t have a strong understanding of the temporal dynamics of this information flow. This thesis aims to improve understanding of the temporal relationship between events, news and associated social media activity. We do this by examining the temporal Twitter response to stimuli for various case studies, primarily based around politics and sporting events. The first part of the thesis focuses on the relationships between Twitter and news media. Using Granger causality, we provide evidence that the social media reaction to events is faster than the traditional news reaction. We also consider how accurately tweet and news volumes can be predicted, given other variables. The second part of the thesis examines information cascades. We show that the decay of retweet rates is well-modelled as a power law with exponential cutoff, providing a better model than the widely used power law. This finding, explained using human prioritisation of tasks, then allows the development of a method to estimate the size of a retweet cascade. The third major part of the thesis concerns tweet clustering methods in response to events. We examine how the likelihood that two tweets are related varies, given the time difference between them, and use this finding to create a clustering method using both textual and temporal information. We also develop a method to estimate the time of the event that caused the corresponding social media reaction.Thesis (Ph.D.) -- University of Adelaide, School of Mathematical Sciences, 201

Combating User Misbehavior on Social Media

Social media encourages user participation and facilitates user’s self-expression like never before. While enriching user behavior in a spectrum of means, many social media platforms have become breeding grounds for user misbehavior. In this dissertation we focus on understanding and combating three specific threads of user misbehaviors that widely exist on social media — spamming, manipulation, and distortion. First, we address the challenge of detecting spam links. Rather than rely on traditional blacklist-based or content-based methods, we examine the behavioral factors of both who is posting the link and who is clicking on the link. The core intuition is that these behavioral signals may be more difficult to manipulate than traditional signals. We find that this purely behavioral approach can achieve good performance for robust behavior-based spam link detection. Next, we deal with uncovering manipulated behavior of link sharing. We propose a four-phase approach to model, identify, characterize, and classify organic and organized groups who engage in link sharing. The key motivating insight is that group-level behavioral signals can distinguish manipulated user groups. We find that levels of organized behavior vary by link type and that the proposed approach achieves good performance measured by commonly-used metrics. Finally, we investigate a particular distortion behavior: making bullshit (BS) statements on social media. We explore the factors impacting the perception of BS and what leads users to ultimately perceive and call a post BS. We begin by preparing a crowdsourced collection of real social media posts that have been called BS. We then build a classification model that can determine what posts are more likely to be called BS. Our experiments suggest our classifier has the potential of leveraging linguistic cues for detecting social media posts that are likely to be called BS. We complement these three studies with a cross-cutting investigation of learning user topical profiles, which can shed light into what subjects each user is associated with, which can benefit the understanding of the connection between user and misbehavior. Concretely, we propose a unified model for learning user topical profiles that simultaneously considers multiple footprints and we show how these footprints can be embedded in a generalized optimization framework. Through extensive experiments on millions of real social media posts, we find our proposed models can effectively combat user misbehavior on social media

Profiling Users and Knowledge Graphs on the Web

Profiling refers to the process of collecting useful information or patterns about something. Due to the growth of the web, profiling methods play an important role in different applications such as recommender systems. In this thesis, we first demonstrate how knowledge graphs (KGs) enhance profiling methods. KGs are databases for entities and their relations. Since KGs have been developed with the objective of information discovery, we assume that they can assist profiling methods. To this end, we develop a novel profiling method using KGs called Hierarchical Concept Frequency-Inverse Document Frequency (HCF-IDF), which combines the strength of traditional term weighting method and semantics in a KG. HCF-IDF represents documents as a set of entities and their weights. We apply HCF-IDF to two applications that recommends researchers and scientific publications. Both applications show HCF-IDF captures topics of documents. As key result, the method can make competitive recommendations based on only the titles of scientific publications, because it reveals relevant entities using the structure of KGs. While the KGs assist profiling methods, we present how profiling methods can improve the KGs. We show two methods that enhance the integrity of KGs. The first method is a crawling strategy that keeps local copies of KGs up-to-date. We profile the dynamics of KGs using a linear regression model. The experiment shows that our novel crawling strategy based on the linear regression model performs better than the state of the art. The second method is a change verification method for KGs. The method classifies each incoming change into a correct or incorrect one to mitigate administrators who check the validity of a change. We profile how topological features influence on the dynamics of a KG. The experiment demonstrates that the novel method using the topological features can improve change verification. Therefore, profiling the dynamics contribute to the integrity of KGs

The Problem of Data Extraction in Social Media: A Theoretical Framework

In today's rapidly evolving digital landscape, the pervasive growth of social media platforms has resulted in an era of unprecedented data generation. These platforms are responsible for generating vast volumes of data on a daily basis, forming intricate webs of patterns and connections that harbor invaluable insights crucial for informed decision-making. Recognizing the significance of exploring social media data, researchers have increasingly turned their attention towards leveraging this data to address a wide array of social research issues. Unlike conventional data collection methods such as questionnaires, interviews, or focus groups, social media data presents unique challenges and opportunities, demanding specialized techniques for its extraction and analysis. However, the absence of a standardized and systematic approach to collect and preprocess social media data remains a gap in the field. This gap not only compromises the quality and credibility of subsequent data analysis but also hinders the realization of the full potential inherent in social media data. This paper aims to bridge this gap by presenting a comprehensive framework designed for the systematic extraction and processing of social media data. The proposed framework offers a clear, step-by-step methodology for the extraction and processing of social media data for analysis. In an era where social media data serves as a pivotal resource for understanding human behavior, sentiment, and societal dynamics, this framework offers a foundational toolset for researchers and practitioners seeking to harness the wealth of insights concealed within the vast expanse of social media data

What demographic attributes do our digital footprints reveal? A systematic review

<div><p>To what extent does our online activity reveal who we are? Recent research has demonstrated that the digital traces left by individuals as they browse and interact with others online may reveal who they are and what their interests may be. In the present paper we report a systematic review that synthesises current evidence on predicting demographic attributes from online digital traces. Studies were included if they met the following criteria: (i) they reported findings where at least one demographic attribute was predicted/inferred from at least one form of digital footprint, (ii) the method of prediction was automated, and (iii) the traces were either visible (e.g. tweets) or non-visible (e.g. clickstreams). We identified 327 studies published up until October 2018. Across these articles, 14 demographic attributes were successfully inferred from digital traces; the most studied included gender, age, location, and political orientation. For each of the demographic attributes identified, we provide a database containing the platforms and digital traces examined, sample sizes, accuracy measures and the classification methods applied. Finally, we discuss the main research trends/findings, methodological approaches and recommend directions for future research.</p></div

A multi-modal, multi-platform, and multi-lingual approach to understanding online misinformation

Due to online social media, access to information is becoming easier and easier. Meanwhile, the truthfulness of online information is often not guaranteed. Incorrect information, often called misinformation, can have several modalities, and it can spread to multiple social media platforms in different languages, which can be destructive to society. However, academia and industry do not have automated ways to assess the impact of misinformation on social media, preventing the adoption of productive strategies to curb the prevalence of misinformation. In this dissertation, I present my research to build computational pipelines that help measuring and detecting misinformation on social media. My work can be divided into three parts. The first part focuses on processing misinformation in text form. I first show how to group political news articles from both trustworthy and untrustworthy news outlets into stories. Then I present a measurement analysis on the spread of stories to characterize how mainstream and fringe Web communities influence each other. The second part is related to analyzing image-based misinformation. It can be further divided into two parts: fauxtography and generic image misinformation. Fauxtography is a special type of image misinformation, where images are manipulated or used out-of-context. In this research, I present how to identify fauxtography on social media by using a fact-checking website (Snopes.com), and I also develop a computational pipeline to facilitate the measurement of these images at scale. I next focus on generic misinformation images related to COVID-19. During the pandemic, text misinformation has been studied in many aspects. However, very little research has covered image misinformation during the COVID-19 pandemic. In this research, I develop a technique to cluster visually similar images together, facilitating manual annotation, to make subsequent analysis possible. The last part is about the detection of misinformation in text form following a multi-language perspective. This research aims to detect textual COVID-19 related misinformation and what stances Twitter users have towards such misinformation in both English and Chinese. To achieve this goal, I experiment on several natural language processing (NLP) models to investigate their performance on misinformation detection and stance detection in both monolingual and multi-lingual manners. The results show that two models: COVID-Tweet-BERT v2 and BERTweet are generally effective in detecting misinformation and stance in the two above manners. These two models are promising to be applied to misinformation moderation on social media platforms, which heavily depends on identifying misinformation and stance of the author towards this piece of misinformation. Overall, the results of this dissertation shed light on understanding of online misinformation, and my proposed computational tools are applicable to moderation of social media, potentially benefitting for a more wholesome online ecosystem

Combating User Misbehavior on Social Media

Social media encourages user participation and facilitates user’s self-expression like never before. While enriching user behavior in a spectrum of means, many social media platforms have become breeding grounds for user misbehavior. In this dissertation we focus on understanding and combating three specific threads of user misbehaviors that widely exist on social media — spamming, manipulation, and distortion. First, we address the challenge of detecting spam links. Rather than rely on traditional blacklist-based or content-based methods, we examine the behavioral factors of both who is posting the link and who is clicking on the link. The core intuition is that these behavioral signals may be more difficult to manipulate than traditional signals. We find that this purely behavioral approach can achieve good performance for robust behavior-based spam link detection. Next, we deal with uncovering manipulated behavior of link sharing. We propose a four-phase approach to model, identify, characterize, and classify organic and organized groups who engage in link sharing. The key motivating insight is that group-level behavioral signals can distinguish manipulated user groups. We find that levels of organized behavior vary by link type and that the proposed approach achieves good performance measured by commonly-used metrics. Finally, we investigate a particular distortion behavior: making bullshit (BS) statements on social media. We explore the factors impacting the perception of BS and what leads users to ultimately perceive and call a post BS. We begin by preparing a crowdsourced collection of real social media posts that have been called BS. We then build a classification model that can determine what posts are more likely to be called BS. Our experiments suggest our classifier has the potential of leveraging linguistic cues for detecting social media posts that are likely to be called BS. We complement these three studies with a cross-cutting investigation of learning user topical profiles, which can shed light into what subjects each user is associated with, which can benefit the understanding of the connection between user and misbehavior. Concretely, we propose a unified model for learning user topical profiles that simultaneously considers multiple footprints and we show how these footprints can be embedded in a generalized optimization framework. Through extensive experiments on millions of real social media posts, we find our proposed models can effectively combat user misbehavior on social media

Characterising semantically coherent classes of text through feature discovery

There is a growing need to provide support for social scientists and humanities scholars to gather and “engage” with very large datasets of free text, to perform very bespoke analyses. method52 is a text analysis platform built for this purpose (Wibberley et al., 2014), and forms a foundation that this thesis builds upon. A central part of method52 and its methodologies is a classifier training component based on dualist (Settles, 2011), and the general process of data engagement with method52 is determined to constitute a continuous cycle of characterising semantically coherent sub-collections, classes, of the text. Two broad methodologies exist for supporting this type of engagement process: (1) a top-down approach wherein concepts and their relationships are explicitly modelled for reasoning, and (2) a more surface-level, bottom-up approach, which entails the use of key terms (surface features) to characterise data. Following the second of these approaches, this thesis examines ways of better supporting this type of data engagement to more effectively support the needs of social scientists and humanities scholars in engaging with text data. The classifier component provides an active learning training environment emphasising the labelling of individual features. However, it can be difficult to interpret and incorporate prior knowledge of features. The process of feature discovery based on the current classifier model does not always produce useful results. And understanding the data well enough to produce successful classifiers is timeconsuming. A new method for discovering features in a corpus is introduced, and feature discovery methods are explored to resolve these issues. When collecting social media data, documents are often obtained by querying an API with a set of key phrases. Therefore, the set of possible classes characterising the data is defined by these basic surface features. It is difficult to know exactly which terms must searched for, and the usefulness of terms can change over time as new discussions and vocabulary emerge. Building on the feature discovery techniques, a framework is presented in this thesis for streaming data with an automatically adapting query to deal with these issues

The laws of "LOL": Computational approaches to sociolinguistic variation in online discussions

When speaking or writing, a person often chooses one form of language over another based on social constraints, including expectations in a conversation, participation in a global change, or expression of underlying attitudes. Sociolinguistic variation (e.g. choosing "going" versus "goin'") can reveal consistent social differences such as dialects and consistent social motivations such as audience design. While traditional sociolinguistics studies variation in spoken communication, computational sociolinguistics investigates written communication on social media. The structured nature of online discussions and the diversity of language patterns allow computational sociolinguists to test highly specific hypotheses about communication, such different configurations of listener "audience." Studying communication choices in online discussions sheds light on long-standing sociolinguistic questions that are hard to tackle, and helps social media platforms anticipate their members' complicated patterns of participation in conversations. To that end, this thesis explores open questions in sociolinguistic research by quantifying language variation patterns in online discussions. I leverage the "birds-eye" view of social media to focus on three major questions in sociolinguistics research relating to authors' participation in online discussions. First, I test the role of conversation expectations in the context of content bans and crisis events, and I show that authors vary their language to adjust to audience expectations in line with community standards and shared knowledge. Next, I investigate language change in online discussions and show that language structure, more than social context, explains word adoption. Lastly, I investigate the expression of social attitudes among multilingual speakers, and I find that such attitudes can explain language choice when the attitudes have a clear social meaning based on the discussion context. This thesis demonstrates the rich opportunities that social media provides for addressing sociolinguistic questions and provides insight into how people adapt to the communication affordances in online platforms.Ph.D