Petr Klan
In this paper, a method of modeling the dynamics of electronic discussions is proposed based on the so called FOPDT model (First Order Plus Dead Time) known from the process control. Knowledge of the model points to possibility of estimating dynamic movements in discussions as well as understanding and designing their maintaining and guidance. Real discussions are processed.
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Social Network Datasets on Reddit Financial Discussion
针对社交媒体金融讨论对股市的影响问题，本文构建了包含Reddit多板块热门meme股相关帖子的新数据集，记录数据处理流程并验证了讨论内容与个股市场走势的相关性。
Online social media (OSM) has a enormous influence in today's world. Some individuals view OSM as fertile ground for abuse and use it to disseminate misinformation and political propaganda, slander competitors, and spread spam. The crowdturfing industry employs large numbers of bots and human workers to manipulate OSM and misrepresent public opinion. The detection of online discussion topics manipulated by OSM \emph{abusers} is an emerging issue attracting significant attention. In this paper, we propose an approach for quantifying the authenticity of online discussions based on the similarity…
Increasingly people form opinions based on information they consume on online social media. As a result, it is crucial to understand what type of content attracts people's attention on social media and drive discussions. In this paper we focus on online discussions. Can we predict which comments and what content gets the highest attention in an online discussion? How does this content differ from community to community? To accomplish this, we undertake a unique study of Reddit involving a large sample comments from 11 popular subreddits with different properties. We introduce a large number of…
Modeling user engagement dynamics on social media has compelling applications in user-persona detection and political discourse mining. Most existing approaches depend heavily on knowledge of the underlying user network. However, a large number of discussions happen on platforms that either lack any reliable social network or reveal only partially the inter-user ties (Reddit, Stackoverflow). Many approaches require observing a discussion for some considerable period before they can make useful predictions. In real-time streaming scenarios, observations incur costs. Lastly, most models do not c…
Cryptocurrencies have recently experienced a new wave of price volatility and interest; activity within social media communities relating to cryptocurrencies has increased significantly. There is currently limited documented knowledge of factors which could indicate future price movements. This paper aims to decipher relationships between cryptocurrency price changes and topic discussion on social media to provide, among other things, an understanding of which topics are indicative of future price movements. To achieve this a well-known dynamic topic modelling approach is applied to social med…
Understanding how social discussion platforms like Reddit are influencing financial behavior
本研究针对金融话题社交平台提出通用的内容与交互分析技术，扩展LDA主题提取方法，设计用户参与度等评估指标，映射用户内容与行为金融学，通过图分析揭示社群形成与信任建立机制，挖掘社交媒体数据的独特洞见。
We study user behavior in the courses offered by a major Massive Online Open Course (MOOC) provider during the summer of 2013. Since social learning is a key element of scalable education in MOOCs and is done via online discussion forums, our main focus is in understanding forum activities. Two salient features of MOOC forum activities drive our research: 1. High decline rate: for all courses studied, the volume of discussions in the forum declines continuously throughout the duration of the course. 2. High-volume, noisy discussions: at least 30% of the courses produce new discussion threads a…
Most previous research on online discussions of atheism has focused on atheism within a Christian context. In contrast, discussions about atheism in the Arab world and from Islamic background are relatively poorly studied. An added complication is that open atheism is against the law in some Arab countries, which may further restrict atheist activity on social media. In this work, we explore atheistic discussion in the Arab Twittersphere. We identify four relevant categories of Twitter users according to the content they post: atheistic, theistic, tanweeri (religious renewal), and other. We ch…
Towards Designing Social Interventions For Online Climate Change Denialism Discussions
本研究提出一种利用内部语言在Reddit气候变化否认主义社区中进行基于证据干预的框架，通过人工与生成式AI结合制作信息并经透明机器人部署，发现中立干预能促进否认者开放讨论及支持者积极回应，为社交媒体干预提供了宝贵见解。
Social media play an increasingly important role in political communication. Various studies investigated how individuals adopt social media for political discussion, to share their views about politics and policy, or to mobilize and protest against social issues. Yet, little attention has been devoted to the main actors of political discussions: the politicians. In this paper, we explore the topics of discussion of U.S. President Obama and the 50 U.S. State Governors using Twitter data and agenda-setting theory as a tool to describe the patterns of daily political discussion, uncovering the m…
Today online social networks have a high impact in our society as more and more people use them for communicating with each other, express their opinions, participating in public discussions, etc. In particular, Twitter is one of the most popular social network platforms people mainly use for political discussions. This attracted the interest of many research studies that analyzed social phenomena on Twitter, by collecting data, analysing communication patterns, and exploring the structure of user networks. While previous works share many common methodologies for data collection and analysis, …
As the use of social platforms continues to evolve, in areas such as cyber-security and defence, it has become imperative to develop adaptive methods for tracking, identifying and investigating cyber-related activities on these platforms. This paper introduces a new approach for detecting cyber-related discussions in online social platforms using a candidate set of terms that are representative of the cyber domain. The objective of this paper is to create a cyber lexicon with cyber-related terms that is applicable to the automatic detection of cyber activities across various online platforms. …
In Bad Faith: Assessing Discussion Quality on Social Media
本文评估社交媒体上主流媒体和政府机构回复的质量，发现背信弃义的互动占比高达68.3%，并探讨其对用户体验的影响。
Multi-Modal Discussion Transformer: Integrating Text, Images and Graph Transformers to Detect Hate Speech on Social Media
本文提出多模态讨论Transformer（mDT），利用图Transformer融合讨论上下文中的文本与图像信息来检测Reddit仇恨言论，并构建新数据集HatefulDiscussions验证其优于仅处理单条评论的基线方法。
A Multi-Level Data-driven Framework for Understanding Perceptions Towards Cycling Infrastructure Across Regions Leveraging Social Media Discourse
本文提出多尺度框架，分析社交媒体上自行车基础设施的讨论和评价，探讨美国与欧洲国家的 sentiment 差异，发现整体 sentiment 为正，但区域间差异较小，讨论内容更批判。
We propose a novel attention based hierarchical LSTM model to classify discourse act sequences in social media conversations, aimed at mining data from online discussion using textual meanings beyond sentence level. The very uniqueness of the task is the complete categorization of possible pragmatic roles in informal textual discussions, contrary to extraction of question-answers, stance detection or sarcasm identification which are very much role specific tasks. Early attempt was made on a Reddit discussion dataset. We train our model on the same data, and present test results on two differen…
We propose an analytical framework able to investigate discussions about polarized topics in online social networks from many different angles. The framework supports the analysis of social networks along several dimensions: time, space and sentiment. We show that the proposed analytical framework and the methodology can be used to mine knowledge about the perception of complex social phenomena. We selected the refugee crisis discussions over Twitter as the case study. This difficult and controversial topic is an increasingly important issue for the EU. The raw stream of tweets is enriched wit…
User dynamics in online social networks have a significant impact on not only the online community but also real-world activities. As examples, we can mention explosive user dynamics triggered by social polarization, echo chamber phenomena, fake news, etc. Explosive user dynamics are frequently called online flaming. The wave equation-based model for online social networks (called the oscillation model) is a theoretical model proposed to describe user dynamics in online social networks. This model can be used to understand the relationship between explosive user dynamics and the structure of s…
Tracking and collecting fast-evolving online discussions provides vast data for studying social media usage and its role in people's public lives. However, collecting social media data using a static set of keywords fails to satisfy the growing need to monitor dynamic conversations and to study fast-changing topics. We propose a dynamic keyword search method to maximize the coverage of relevant information in fast-evolving online discussions. The method uses word embedding models to represent the semantic relations between keywords and predictive models to forecast the future time series. We a…
Online social network (OSN) discussion groups are exerting significant effects on political dialogue. In the absence of access control mechanisms, any user can contribute to any OSN thread. Individuals can exploit this characteristic to execute targeted attacks, which increases the potential for subsequent malicious behaviors such as phishing and malware distribution. These kinds of actions will also disrupt bridges among the media, politicians, and their constituencies. For the concern of Security Management, blending malicious cyberattacks with online social interactions has introduced a bra…
The pandemic required efficient allocation of public resources and transforming existing ways of societal functions. To manage any crisis, governments and public health researchers exploit the information available to them in order to make informed decisions, also defined as situational awareness. Gathering situational awareness using social media has been functional to manage epidemics. Previous research focused on using discussions during periods of epidemic crises on social media platforms like Twitter, Reddit, or Facebook and developing NLP techniques to filter out relevant discussions fro…
Persuasion and argumentation are possibly among the most complex examples of the interplay between multiple human subjects. With the advent of the Internet, online forums provide wide platforms for people to share their opinions and reasonings around various diverse topics. In this work, we attempt to model persuasive interaction between users on Reddit, a popular online discussion forum. We propose a deep LSTM model to classify whether a conversation leads to a successful persuasion or not, and use this model to predict whether a certain chain of arguments can lead to persuasion. While learni…
Online discussion forum creates an asynchronous conversation environment for online users to exchange ideas and share opinions through a unique thread-reply communication mode. Accurately modeling information dynamics under such a mode is important, as it provides a means of mining latent spread patterns and understanding user behaviors. In this paper, we design a novel temporal point process model to characterize information cascades in online discussion forums. The proposed model views the entire event space as a nested structure composed of main thread streams and their linked reply streams…
Multifaceted polarisation and information reliability in climate change discussions on social media platforms
该研究分析Twitter互动拓扑及跨平台分享，发现气候变化讨论存在意识形态、结构与情绪交织的多面极化，右倾社群更易传播低可靠性信息。
The Future of Research on Social Technologies: CCC Workshop Visioning Report
本报告基于2023年CCC跨学科社交技术研究愿景研讨会的讨论，梳理现有研究空白，明确未来需重点投入的方向及成果转化路径，为社交技术的设计、治理与研究发展提供指引。
The emergence of social media has largely eased the way people receive information and participate in public discussions. However, in countries with strict regulations on discussions in the public space, social media is no exception. To limit the degree of dissent or inhibit the spread of "harmful" information, a common approach is to impose information operations such as censorship/suspension on social media. In this paper, we focus on a study of censorship on Weibo, the counterpart of Twitter in China. Specifically, we 1) create a web-scraping pipeline and collect a large dataset solely focu…
On the Joint Effect of Culture and Discussion Topics on X (Twitter) Signed Ego Networks
本文通过26个数据集分析文化与讨论话题对X（推特）签名自我中心网络情感的影响，发现文化影响不易被话题压倒，且消极性在ENM各层级稳定，通用话题数量可预测关系积极性。
Exponential growth of social media consumption demands effective user interests-social contents modeling for more personalized recommendation and social media summarization. However, due to the heterogeneous nature of social contents, traditional approaches lack the ability of capturing the hidden semantic correlations across these multi-modal data, which leads to semantic gaps between social content understanding and user interests. To effectively bridge the semantic gaps, we propose a novel deep learning framework for user interests-social contents modeling. We first mine and parse data, i.e…
Exploring Public Perceptions of Generative AI in Libraries: A Social Media Analysis of X Discussions
本研究通过分析X平台帖子，采用混合方法揭示公众对图书馆中生成式AI的认知以负面情绪为主，并识别出关键参与者和跨领域互动结构。
Health-related rumors spreading online during a public crisis may pose a serious threat to people's well-being. Existing crisis informatics research lacks in-depth insights into the characteristics of health rumors and the efforts to debunk them on social media in a pandemic. To fill this gap, we conduct a comprehensive analysis of four months of rumor-related online discussion during COVID-19 on Weibo, a Chinese microblogging site. Results suggest that the dread (cause fear) type of health rumors provoked significantly more discussions and lasted longer than the wish (raise hope) type. We fur…
Visualizing the Evolution of Twitter (X.com) Conversations: A Comprehensive Methodology Applied to AI Training Discussions on ChatGPT
本文提出一种结合 Python 与 R 的综合方法，用于提取、情感分析、关键词编码并可视化 X.com 对话，并以 ChatGPT 相关 AI 发展暂停讨论为例，揭示社交网络动态与影响力演化。
Analyzing Crime Discourse in U.S. Metropolitan Communities on Reddit: Trends, Influences, and Insights
本文研究Reddit上384个美国大都会社区的犯罪讨论，发现大型社区和政治倾向更自由的社区犯罪讨论更频繁，但犯罪率与讨论频率无强相关性。
Online discussion platforms offer a forum to strengthen and propagate belief in misinformed conspiracy theories. Yet, they also offer avenues for conspiracy theorists to express their doubts and experiences of cognitive dissonance. Such expressions of dissonance may shed light on who abandons misguided beliefs and under which circumstances. This paper characterizes self-disclosures of dissonance about QAnon, a conspiracy theory initiated by a mysterious leader Q and popularized by their followers, anons in conspiracy theory subreddits. To understand what dissonance and disbelief mean within co…
Nowadays, massive useful data of user information and social behavior have been accumulated on the Internet, providing a possibility of profiling user's personality traits online. In this paper, we propose a psychological modeling method based on computational linguistic features to profile Big Five personality traits of users on Sina Weibo (a Twitter-like microblogging service in China) and their correlations with user's social behaviors. To the best of our knowledge, this is the first research on investigating the potential relationship between profile information, social-network behaviors a…
In many domains of life, business and management, numerous problems are addressed by small groups of individuals engaged in face-to-face discussions. While research in social psychology has a long history of studying the determinants of small group performances, the internal dynamics that govern a group discussion is not yet well understood. Here, we rely on computational methods based on network analyses and opinion dynamics to described how individuals influence each other during a group discussion. We consider the situation in which a small group of three individuals engages in a discussion…
Emergent Influence Networks in Good-Faith Online Discussions
本研究结合社会网络分析与机器学习，提出新框架证明在线讨论中用户网络位置影响说服力，且成功说服的认可会强化其主导地位，为理解数字空间中的话语动态提供洞见。
Causal Effects of Trigger Words in Social Media Discussions: A Large-Scale Case Study about UK Politics on Reddit
本研究通过分析Reddit上的英国政治讨论，发现触发词与高参与度和负面情绪相关，揭示了社交媒体中极化现象的机制。
With an expansive and ubiquitously available gold mine of educational data, Massive Open Online courses (MOOCs) have become the an important foci of learning analytics research. In this paper, we investigate potential reasons as to why are these digitalized learning repositories being plagued with huge attrition rates. We analyze an ongoing online course offered in Coursera using a social network perspective, with an objective to identify students who are actively participating in course discussions and those who are potentially at a risk of dropping off. We additionally perform extensive foru…
Hashtags play a cardinal role in the classification of topics over social media. A sudden burst on the usage of certain hashtags, representing specific topics, give rise to trending topics. Trending topics can be immensely useful as it can spark a discussion on a particular subject. However, it can also be used to suppress an ongoing pivotal matter. This paper discusses how a significant economic crisis was covered by triggering a current trending topic. A case study on politics in India has been studied over the past two months. The analysis shows how the issue on inflation was attacked by th…
For many European countries, in 2015 the refugee situation developed from a remote tragedy reported upon in the news to a situation they have to deal with in their own neighborhood. Driven by this observation, we investigated the development of the perception of the refugee situation during 2015 in Twitter. Starting from a dataset of 1.7 Million tweets covering refugee-related topics from May to December 2015, we investigated how the discussion on refugees changed over time, in different countries as well as in relationship with the evolution of the actual situation. In this paper we report an…
This article examines the principles outlined in the General Data Protection Regulation (GDPR) in the context of social network data. We provide both a practical guide to GDPR-compliant social network data processing, covering aspects such as data collection, consent, anonymization and data analysis, and a broader discussion of the problems emerging when the general principles on which the regulation is based are instantiated to this research area.
The rapid growth of location-based services(LBSs)has greatly enriched people's urban lives and attracted millions of users in recent years. Location-based social networks(LBSNs)allow users to check-in at a physical location and share daily tips on points-of-interest (POIs) with their friends anytime and anywhere. Such check-in behavior can make daily real-life experiences spread quickly through the Internet. Moreover, such check-in data in LBSNs can be fully exploited to understand the basic laws of human daily movement and mobility. This paper focuses on reviewing the taxonomy of user modelin…
A methodology for analyzing financial needs hierarchy from social discussions using LLM
本文提出利用大语言模型分析社交媒体中财务需求层级结构，通过计算验证其层级组织特征，并提供新的在线财务讨论内容洞察。
Online antisocial behavior, such as cyberbullying, harassment, and trolling, is a widespread problem that threatens free discussion and has negative physical and mental health consequences for victims and communities. While prior work has proposed automated methods to identify hostile comments in online discussions, these methods work retrospectively on comments that have already been posted, making it difficult to intervene before an interaction escalates. In this paper we instead consider the problem of forecasting future hostilities in online discussions, which we decompose into two tasks: …
Social Media can be used to extract discussion topics during a disaster. With the COVID-19 pandemic impact on South Africa, we need to understand how the law and regulation promulgated by the government in response to the pandemic contrasts with discussion topics social media users have been engaging in. In this work, we expand on traditional media analysis by using Social Media discussions driven by or directed to South African government officials. We find topics that are similar as well as different in some cases. The findings can inform further study into social media during disaster setti…
One major feature of social networks (e.g., massive online social networks) is the dissemination of information, such as news, rumors and opinions. Information can be propagated via natural connections in written, oral or electronic forms. The physics of information diffusion has been changed with the mainstream adoption of the Internet and Web. Until a few years ago, the major barrier for someone who wanted a piece of information to spread through a community was the cost of the technical infrastructure required to reach a large number of people. Today, with widespread access to the Internet,…
Malicious actors exploit social media to inflate stock prices, sway elections, spread misinformation, and sow discord. To these ends, they employ tactics that include the use of inauthentic accounts and campaigns. Methods to detect these abuses currently rely on features specifically designed to target suspicious behaviors. However, the effectiveness of these methods decays as malicious behaviors evolve. To address this challenge, we propose a general language for modeling social media account behavior. Words in this language, called BLOC, consist of symbols drawn from distinct alphabets repre…
A Twitter narrative of the COVID-19 pandemic in Australia
本研究分析2020年1月至2022年10月澳大利亚COVID-19相关Twitter讨论，运用监督与无监督前沿方法开展多维度分析，探索讨论动态以助力疫情管理自动化信息系统开发。
In this work, we present a novel quantification of conflict in online discussion. Unlike previous studies on conflict dynamics, which model conflict as a binary phenomenon, our measure is continuous-valued, which we validate with manually annotated ratings. We address a two-way prediction task. Firstly, we predict the probable degree of conflict a news article will face from its audience. We employ multiple machine learning frameworks for this task using various features extracted from news articles. Secondly, given a pair of users and their interaction history, we predict if their future enga…
Can Generative Agent-Based Modeling Replicate the Friendship Paradox in Social Media Simulations?
本文提出用于社交媒体仿真的生成式智能体建模框架，并基于美国2020年大选和QAnon数据集验证友谊悖论能够自然涌现，表明GABM可有效建模全局网络动态。
Studies of online social influence have demonstrated that friends have important effects on many types of behavior in a wide variety of settings. However, we know much less about how influence works among relative strangers in digital public squares, despite important conversations happening in such spaces. We present the results of a study on large public Facebook pages where we randomly used two different methods--most recent and social feedback--to order comments on posts. We find that the social feedback condition results in higher quality viewed comments and response comments. After measu…
A significant share of political discourse occurs online on social media platforms. Policymakers and researchers try to understand the role of social media design in shaping the quality of political discourse around the globe. In the past decades, scholarship on political discourse theory has produced distinct characteristics of different types of prominent political rhetoric such as deliberative, civic, or demagogic discourse. This study investigates the relationship between social media reaction mechanisms (i.e., upvotes, downvotes) and political rhetoric in user discussions by engaging in a…
This paper describes a Social Network Analysis toolkit to monitor an Enterprise Social Network and help analyzing informal leadership as a function of social ties and topic discussions. The toolkit has been developed in the context of a regional project, Fiordaliso, funded by Regione Lazio (a region of central Italy) and leaded by Reply, an international network of specialized companies in the field of digital services.
LLMs for Social Network Modeling: From Network Generation to Dynamic Processes
本综述首次系统梳理大语言模型在社交网络建模中的应用，将其分为网络生成与动态过程两类，并探讨了偏见与提示敏感性等挑战及未来方向。
Social-LLM: Modeling User Behavior at Scale using Language Models and Social Network Data
针对大规模社交网络建模的可扩展性挑战，提出Social-LLM方法，结合局部社交网络交互与大型语言模型，在七个真实数据集上验证了其在用户检测任务中的有效性。
Online discourse covers a wide range of topics and many actors tailor their content to impact online discussions through carefully crafted messages and targeted campaigns. Yet the scale and diversity of online media content make it difficult to evaluate the impact of a particular message. In this paper, we present a new technique that leverages semantic similarity to quantify the change in the discussion after a particular message has been published. We use a set of press releases from environmental organisations and tweets from the climate change debate to show that our novel approach reveals…
We analyze the structure and evolution of discussion cascades in four popular websites: Slashdot, Barrapunto, Meneame and Wikipedia. Despite the big heterogeneities between these sites, a preferential attachment (PA) model with bias to the root can capture the temporal evolution of the observed trees and many of their statistical properties, namely, probability distributions of the branching factors (degrees), subtree sizes and certain correlations. The parameters of the model are learned efficiently using a novel maximum likelihood estimation scheme for PA and provide a figurative interpretat…
Leveraging LLMs for Mental Health: Detection and Recommendations from Social Discussions
本文提出一个综合框架，结合 NLP 和生成式 AI 技术，利用基于规则和预训练模型及 LLMs 微调的混合方法，从 Reddit 帖子中识别心理健康障碍并评估严重程度，以提供行为改变和治疗干预建议，促进早期检测与个性化护理。
Characterizing User Archetypes and Discussions on Scored.co
本文提出一个多维框架，利用超网络表示和用户活动、情感、毒性等特征刻画 Scored.co 上的用户原型，并分析其动态、互动与影响力，揭示高阶互动对理解在线社交动态的重要性。
Social learning, i.e., students learning from each other through social interactions, has the potential to significantly scale up instruction in online education. In many cases, such as in massive open online courses (MOOCs), social learning is facilitated through discussion forums hosted by course providers. In this paper, we propose a probabilistic model for the process of learners posting on such forums, using point processes. Different from existing works, our method integrates topic modeling of the post text, timescale modeling of the decay in post activity over time, and learner topic in…
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