Online Social Networks and Media最新文献

How does user-generated content on Social Media affect stock predictions? A case study on GameStop 社交媒体上的用户生成内容如何影响股票预测？GameStop 案例研究

Online Social Networks and Media Pub Date : 2024-11-01 DOI: 10.1016/j.osnem.2024.100293

Antonino Ferraro , Giancarlo Sperlì

{"title":"How does user-generated content on Social Media affect stock predictions? A case study on GameStop","authors":"Antonino Ferraro , Giancarlo Sperlì","doi":"10.1016/j.osnem.2024.100293","DOIUrl":"10.1016/j.osnem.2024.100293","url":null,"abstract":"<div><div>One of the main challenges in the financial market concerns the forecasting of stock behavior, which plays a key role in supporting the financial decisions of investors. In recent years, the large amount of available financial data and the heterogeneous contextual information led researchers to investigate data-driven models using Artificial Intelligence (AI)-based approaches for forecasting stock prices. Recent methodologies focus mainly on analyzing participants from Reddit without considering other social media and how their combination affects the stock market, which remains an open challenge. In this paper, we combine financial data and textual user-generated information, which are provided as input to various deep learning models, to develop a stock forecasting system. The main novelties of the proposal concern the design of a multi-modal approach combining historical stock prices and sentiment scores extracted by different Online Social Networks (OSNs), also unveiling possible correlations about heterogeneous information evaluated during the GameStop squeeze. In particular, we have examined several AI-based models and investigated the impact of textual data inferred from well-known Online Social Networks (<em>i.e.</em>, Reddit and Twitter) on stock market behavior by conducting a case study on GameStop. Although users’ dynamic opinions on social networks may have a detrimental impact on the stock prediction task, our investigation has demonstrated the usefulness of assessing user-generated content inferred from various OSNs on the market forecasting problem.</div></div>","PeriodicalId":52228,"journal":{"name":"Online Social Networks and Media","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142653576","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Measuring centralization of online platforms through size and interconnection of communities 通过社区规模和相互联系衡量在线平台的集中化程度

Online Social Networks and Media Pub Date : 2024-10-25 DOI: 10.1016/j.osnem.2024.100292

Milo Z. Trujillo, Laurent Hébert-Dufresne, James Bagrow

{"title":"Measuring centralization of online platforms through size and interconnection of communities","authors":"Milo Z. Trujillo, Laurent Hébert-Dufresne, James Bagrow","doi":"10.1016/j.osnem.2024.100292","DOIUrl":"10.1016/j.osnem.2024.100292","url":null,"abstract":"<div><div>Decentralization of online social platforms offers a variety of potential benefits, including divesting of moderator and administrator authority among a wider population, allowing a variety of communities with differing social standards to coexist, and making the platform more resilient to technical or social attack. However, a platform offering a decentralized architecture does not guarantee that users will use it in a decentralized way, and measuring the centralization of socio-technical networks is not an easy task. In this paper we introduce a method of characterizing inter-community influence, to measure the impact that removing a community would have on the remainder of a platform. Our approach provides a careful definition of “centralization” appropriate in bipartite user-community socio-technical networks, and demonstrates the inadequacy of more trivial methods for interrogating centralization such as examining the distribution of community sizes. We use this method to compare the structure of five socio-technical platforms, and find that even decentralized platforms like Mastodon are far more centralized than any synthetic networks used for comparison. We discuss how this method can be used to identify when a platform is more centralized than it initially appears, either through inherent social pressure like assortative preferential attachment, or through astroturfing by platform administrators, and how this knowledge can inform platform governance and user trust.</div></div>","PeriodicalId":52228,"journal":{"name":"Online Social Networks and Media","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142530114","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Crowdsourcing the Mitigation of disinformation and misinformation: The case of spontaneous community-based moderation on Reddit 众包减少虚假信息和错误信息：Reddit 上基于社区的自发审核案例

Online Social Networks and Media Pub Date : 2024-10-19 DOI: 10.1016/j.osnem.2024.100291

Giulio Corsi , Elizabeth Seger , Sean Ó hÉigeartaigh

{"title":"Crowdsourcing the Mitigation of disinformation and misinformation: The case of spontaneous community-based moderation on Reddit","authors":"Giulio Corsi , Elizabeth Seger , Sean Ó hÉigeartaigh","doi":"10.1016/j.osnem.2024.100291","DOIUrl":"10.1016/j.osnem.2024.100291","url":null,"abstract":"<div><div>Community-based content moderation, an approach that utilises user-generated knowledge to shape the ranking and display of online content, is recognised as a potential tool in combating disinformation and misinformation. This study examines this phenomenon on Reddit, which employs a platform-wide content ranking system based on user upvotes and downvotes. By empowering users to influence content visibility, Reddit's system serves as a naturally occurring community moderation mechanism, providing an opportunity to analyse how users engage with this system. Focusing on discussions related to climate change, we observe that in this domain, low-credibility content is spontaneously moderated by Reddit users, although the magnitude of this effect varies across Subreddits. We also identify temporal fluctuations in content removal rates, indicating dynamic and context-dependent patterns influenced by platform policies and socio-political factors. These findings highlight the potential of community-based moderation in mitigating online false information, offering valuable insights for the development of robust social media moderation frameworks.</div></div>","PeriodicalId":52228,"journal":{"name":"Online Social Networks and Media","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142530113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

GASCOM: Graph-based Attentive Semantic Context Modeling for Online Conversation Understanding GASCOM：基于图的细心语义上下文建模用于在线对话理解

Online Social Networks and Media Pub Date : 2024-10-16 DOI: 10.1016/j.osnem.2024.100290

Vibhor Agarwal , Yu Chen , Nishanth Sastry

{"title":"GASCOM: Graph-based Attentive Semantic Context Modeling for Online Conversation Understanding","authors":"Vibhor Agarwal , Yu Chen , Nishanth Sastry","doi":"10.1016/j.osnem.2024.100290","DOIUrl":"10.1016/j.osnem.2024.100290","url":null,"abstract":"<div><div>Online conversation understanding is an important yet challenging NLP problem which has many useful applications (e.g., hate speech detection). However, online conversations typically unfold over a series of posts and replies to those posts, forming a tree structure within which individual posts may refer to semantic context from elsewhere in the tree. Such semantic cross-referencing makes it difficult to understand a single post by itself; yet considering the entire conversation tree is not only difficult to scale but can also be misleading as a single conversation may have several distinct threads or points, not all of which are relevant to the post being considered. In this paper, we propose a <strong>G</strong>raph-based <strong>A</strong>ttentive <strong>S</strong>emantic <strong>CO</strong>ntext <strong>M</strong>odeling (GASCOM) framework for online conversation understanding. Specifically, we design two novel algorithms that utilize both the graph structure of the online conversation as well as the semantic information from individual posts for retrieving relevant context nodes from the whole conversation. We further design a <em>token-level</em> multi-head graph attention mechanism to pay different attentions to different tokens from different selected context utterances for fine-grained conversation context modelling. Using this semantic conversational context, we re-examine two well-studied problems: polarity prediction and hate speech detection. Our proposed framework significantly outperforms state-of-the-art methods on both tasks, improving macro-F1 scores by 4.5% for polarity prediction and by 5% for hate speech detection. The GASCOM context weights also enhance interpretability.</div></div>","PeriodicalId":52228,"journal":{"name":"Online Social Networks and Media","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142441667","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The influence of coordinated behavior on toxicity 协调行为对毒性的影响

Online Social Networks and Media Pub Date : 2024-10-03 DOI: 10.1016/j.osnem.2024.100289

Edoardo Loru , Matteo Cinelli , Maurizio Tesconi , Walter Quattrociocchi

引用次数: 0

Friend2User : A new CNN based method for user network and content embedding Friend2User：基于 CNN 的用户网络和内容嵌入新方法

Online Social Networks and Media Pub Date : 2024-09-27 DOI: 10.1016/j.osnem.2024.100288

Amal Rekik, Salma Jamoussi

{"title":"Friend2User : A new CNN based method for user network and content embedding","authors":"Amal Rekik, Salma Jamoussi","doi":"10.1016/j.osnem.2024.100288","DOIUrl":"10.1016/j.osnem.2024.100288","url":null,"abstract":"<div><div>Nowadays, social networks have become an integral part of modern society, significantly influencing individuals worldwide due to their extensive reach. Consequently, analyzing the data disseminated within these networks in order to identify online communities presents a major challenge for researchers in the data mining field. To address this challenge, we propose, in this paper, a novel deep user embedding framework for community extraction on social networks. Our method leverages the capability of Convolutional Neural Networks (CNNs) to produce abstract representations of users that preserve the semantic information in the data. Specifically, our approach considers both the profile content and the network structure, harnessing the power of unsupervised CNNs. The key concept underlying our proposal is that each user is represented not only by their own content but also by the content of their close friends. We employ a recursive CNN to integrate neighboring users’ content, thereby generating concise and informative user embeddings. The empirical findings obtained by our method demonstrate the effectiveness of our proposed user embeddings in efficiently detecting communities within social networks, particularly in the context of cybersecurity.</div></div>","PeriodicalId":52228,"journal":{"name":"Online Social Networks and Media","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142327284","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Cross-community affinity: A polarization measure for multi-community networks 跨社区亲和力：多社区网络的极化衡量标准

Online Social Networks and Media Pub Date : 2024-08-21 DOI: 10.1016/j.osnem.2024.100280

Sreeja Nair , Adriana Iamnitchi

{"title":"Cross-community affinity: A polarization measure for multi-community networks","authors":"Sreeja Nair , Adriana Iamnitchi","doi":"10.1016/j.osnem.2024.100280","DOIUrl":"10.1016/j.osnem.2024.100280","url":null,"abstract":"<div><p>This article introduces a heterophily-based metric for assessing polarization in social networks when different opposing ideological communities coexist. The proposed metric measures polarization at the node level and is based on a node’s affinity for other communities. Node-level values can then be aggregated at the community, network, or any intermediate level, resulting in a more comprehensive map of polarization. We looked at our metric on the Polblogs network, the White Helmets Twitter interaction network with two communities, and the VoterFraud2020 domain network with five communities. Additionally, we evaluated our metric on different sets of synthetic graphs to confirm that it yields low polarization scores, as expected. We employed three ways to build synthetic networks: synthetic labeling, dK-series, and network models, in order to assess how the proposed measure behaves to various topologies and network features. Then, we compared our metric to two commonly used polarization metrics, Guerra’s boundary polarization and the random walk controversy score. We also examined how our suggested metric correlates with two network metrics: assortativity and modularity.</p></div>","PeriodicalId":52228,"journal":{"name":"Online Social Networks and Media","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142021566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

RICo: Reddit ideological communities RICo：Reddit 意识形态社区

Online Social Networks and Media Pub Date : 2024-06-21 DOI: 10.1016/j.osnem.2024.100279

Kamalakkannan Ravi, Adan Ernesto Vela

{"title":"RICo: Reddit ideological communities","authors":"Kamalakkannan Ravi, Adan Ernesto Vela","doi":"10.1016/j.osnem.2024.100279","DOIUrl":"https://doi.org/10.1016/j.osnem.2024.100279","url":null,"abstract":"<div><p>The main objective of our research is to gain a comprehensive understanding of the relationship between language usage within different communities and delineating the ideological narratives. We focus specifically on utilizing Natural Language Processing techniques to identify underlying narratives in the coded or suggestive language employed by non-normative communities associated with targeted violence. Earlier studies addressed the detection of ideological affiliation through surveys, user studies, and a limited number based on the content of text articles, which still require label curation. Previous work addressed label curation by using ideological subreddits (<em>r/Liberal</em> and <em>r/Conservative</em> for Liberal and Conservative classes) to label the articles shared on those subreddits according to their prescribed ideologies, albeit with a limited dataset.</p><p>Building upon previous work, we use subreddit ideologies to categorize shared articles. In addition to the conservative and liberal classes, we introduce a new category called “Restricted” which encompasses text articles shared in subreddits that are restricted, privatized, or banned, such as <em>r/TheDonald</em>. The “Restricted” class encompasses posts tied to violence, regardless of conservative or liberal affiliations. Additionally, we augment our dataset with text articles from self-identified subreddits like <em>r/progressive</em> and <em>r/askaconservative</em> for the liberal and conservative classes, respectively. This results in an expanded dataset of 377,144 text articles, consisting of 72,488 liberal, 79,573 conservative, and 225,083 restricted class articles. Our goal is to analyze language variances in different ideological communities, investigate keyword relevance in labeling article orientations, especially in unseen cases (922,522 text articles), and delve into radicalized communities, conducting thorough analysis and interpretation of the results.</p></div>","PeriodicalId":52228,"journal":{"name":"Online Social Networks and Media","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141438831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evaluating password strength based on information spread on social networks: A combined approach relying on data reconstruction and generative models 根据社交网络上传播的信息评估密码强度：依靠数据重建和生成模型的组合方法

Online Social Networks and Media Pub Date : 2024-06-14 DOI: 10.1016/j.osnem.2024.100278

Maurizio Atzori , Eleonora Calò , Loredana Caruccio , Stefano Cirillo , Giuseppe Polese , Giandomenico Solimando

{"title":"Evaluating password strength based on information spread on social networks: A combined approach relying on data reconstruction and generative models","authors":"Maurizio Atzori , Eleonora Calò , Loredana Caruccio , Stefano Cirillo , Giuseppe Polese , Giandomenico Solimando","doi":"10.1016/j.osnem.2024.100278","DOIUrl":"https://doi.org/10.1016/j.osnem.2024.100278","url":null,"abstract":"<div><p>Ensuring the security of personal accounts has become a key concern due to the widespread password attack techniques. Although passwords are the primary defense against unauthorized access, the practice of reusing easy-to-remember passwords increases security risks for people. Traditional methods for evaluating password strength are often insufficient since they overlook the public personal information that users frequently share on social networks. In addition, while users tend to limit access to their data on single profiles, personal data is often unintentionally shared across multiple profiles, exposing users to password threats. In this paper, we present an extension of a data reconstruction tool, namely <span>soda</span> <span>advance</span>, which incorporates a new module to evaluate password strength based on publicly available data across multiple social networks. It relies on a new metric to provide a comprehensive evaluation of password strength. Moreover, we investigate the capabilities and risks associated with emerging Large Language Models (LLMs) in evaluating and generating passwords, respectively. Specifically, by exploiting the proliferation of LLMs, it has been possible to interact with many LLMs through Automated Template Learning methodologies. Experimental evaluations, performed with 100 real users, demonstrate the effectiveness of LLMs in generating strong passwords with respect to data associated with users’ profiles. Furthermore, LLMs have proved to be effective also in evaluation tasks, but the combined usage of LLMs and <span>soda</span> <span>advance</span> guaranteed better classifications up to more than 10% in terms of F1-score.</p></div>","PeriodicalId":52228,"journal":{"name":"Online Social Networks and Media","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S246869642400003X/pdfft?md5=d155f83a585842083bfff6fb44108b0f&pid=1-s2.0-S246869642400003X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141324524","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Exploring the journey of influencers in shaping social media engagement success 探索影响者塑造社交媒体参与成功的历程

Online Social Networks and Media Pub Date : 2024-05-01 DOI: 10.1016/j.osnem.2024.100277

Pouyan Eslami, Mahdi Najafabadi, Amir Gharehgozli

{"title":"Exploring the journey of influencers in shaping social media engagement success","authors":"Pouyan Eslami, Mahdi Najafabadi, Amir Gharehgozli","doi":"10.1016/j.osnem.2024.100277","DOIUrl":"https://doi.org/10.1016/j.osnem.2024.100277","url":null,"abstract":"<div><p>This study unfolds nuanced insights into the diverse dimensions dictating the success of social media influencers. Analyzing more than 210,000 social media posts and utilizing the Heuristic-Systematic Model of Information Processing (HSM), this study explores diverse factors, including individual appearance characteristics, depth of persuasive power, and various influencer types. The findings of this study shed light on the distinct impacts of varying influencer archetypes, such as celebrities and micro-celebrities, on user engagement and reveal the nuanced moderating effects of these archetypes on the relationships intertwined with personal attributes, persuasive potency, and influencer success. The proposed model advocates that influencers who leverage more profound, systematic processing strategies, marked by detailed information analysis and conveyance, are poised to experience elevated user engagement compared to counterparts employing heuristic modalities, distinguished by practical mental shortcuts and superficial examinations. This elucidation accentuates the imperative of harmonizing heuristic and systematic methodologies for emerging influencers and brands aspiring to optimize user engagement and efficaciously mold consumer behavior. This paper encapsulates a comprehensive exploration of the dynamic landscapes of influencer marketing via the HSM prism, delivering profound insights and practical ramifications for scholars, marketers, and influencers aiming to navigate and exploit the intricate networks of influential determinants in the ever-evolving digital marketing domain.</p></div>","PeriodicalId":52228,"journal":{"name":"Online Social Networks and Media","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2468696424000028/pdfft?md5=1f97071692e10b0a65a5cd8d1be228ce&pid=1-s2.0-S2468696424000028-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140917740","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0