Online Social Networks and Media最新文献

This editorial article introduces the OSNEM special issue on Detecting, Understanding and Countering Online Harms. Whilst online social networks and media have revolutionised society, leading to unprecedented connectivity across the globe, they have also enabled the spread of hazardous and dangerous behaviours. Such ‘online harms’ are now a pressing concern for policymakers, regulators and big tech companies. Building deep knowledge about the scope, nature, prevalence, origins and dynamics of online harms is crucial for ensuring we can clean up online spaces. This, in turn, requires innovation and advances in methods, data, theory and research design – and developing multi-domain and multi-disciplinary approaches. In particular, there is a real need for methodological research that develops high-quality methods for detecting online harms in a robust, fair and explainable way. With this motivation in mind, the present special issue attracted 20 submissions, of which 8 were ultimately accepted for publication in the journal. These submissions predominantly revolve around online misinformation and abusive language, with an even distribution between the two topics. In what follows, we introduce and briefly discuss the contributions of these accepted submissions.

The COVID-19 pandemic caused high uncertainty regarding appropriate treatments and public policy reactions. This uncertainty provided a perfect breeding ground for spreading conspiratorial anti-science narratives based on disinformation. Disinformation on public health may alter the population’s hesitance to vaccinations, counted among the ten most severe threats to global public health by the United Nations. We understand conspiracy narratives as a combination of disinformation, misinformation, and rumour that are especially effective in drawing people to believe in post-factual claims and form disinformed social movements. Conspiracy narratives provide a pseudo-epistemic background for disinformed social movements that allow for self-identification and cognitive certainty in a rapidly changing information environment. This study monitors two established conspiracy narratives and their communities on Twitter, the anti-vaccination and anti-5G communities, before and during the first UK lockdown. The study finds that, despite content moderation efforts by Twitter, conspiracy groups were able to proliferate their networks and influence broader public discourses on Twitter, such as #Lockdown in the United Kingdom.

Social media platforms are increasingly used to convey advertising campaigns for products or services. A key issue is to identify an appropriate set of influencers within a social network, investing resources to get them to adopt a product. Influence maximization is an optimization problem that aims at finding a small set of users that maximize the spread of influence in a social network. In this paper we propose an influence maximization algorithm, named Weighted Artificial Bee Colony (WABC), that is based on a bio-inspired technique for identifying a subset of users which maximizes the spread. The proposed algorithm has been applied to a case study that analyzes the propagation of information among Twitter users during the Constitutional Referendum held in Italy in 2016. Our analysis is aimed at identifying the main influencers of the $yes$ and $no$ factions, and deriving the main information diffusion strategies of each faction during the political campaign. WABC outperformed ranking-proxy techniques based on classical centrality measures, i.e., PageRank, Rank and Degree. Even compared to DIRIE, which exploits a more complex algorithm, WABC was able to find a more accurate set of users which allows to maximize the spread in almost all the considered configurations.

During the coronavirus disease 2019 (COVID-19) pandemic, the video-sharing platform YouTube has been serving as an essential instrument to widely distribute news related to the global public health crisis and to allow users to discuss the news with each other in the comment sections. Along with these enhanced opportunities of technology-based communication, there is an overabundance of information and, in many cases, misinformation about current events. In times of a pandemic, the spread of misinformation can have direct detrimental effects, potentially influencing citizens' behavioral decisions (e.g., to not socially distance) and putting collective health at risk. Misinformation could be especially harmful if it is distributed in isolated news cocoons that homogeneously provide misinformation in the absence of corrections or mere accurate information. The present study analyzes data gathered at the beginning of the pandemic (January–March 2020) and focuses on the network structure of YouTube videos and their comments to understand the level of informational homogeneity associated with misinformation on COVID-19 and its evolution over time. This study combined machine learning and network analytic approaches. Results indicate that nodes (either individual users or channels) that spread misinformation were usually integrated in heterogeneous discussion networks, predominantly involving content other than misinformation. This pattern remained stable over time. Findings are discussed in light of the COVID-19 “infodemic” and the fragmentation of information networks.

During the 2016 US presidential election, we witnessed a polarized population and an election outcome that defied the predictions of many media sources. In this study, we conducted a follow-up on political view migration through tracking Twitter users’ account activity. The study was conducted by following a set of Twitter users over a four year period. Each year, Twitter user activities were collected and analyzed by our novel cross-account data mining algorithm. This algorithm through multiple iterations computes a numerical political score for each user based on their connection to other users and hashtags. We identified a set of seed users and hashtags using prominent political figures and movements to bootstrap the algorithm. The political score distribution demonstrates a divided population on political views. We also observed that users are more moderate in years close to elections (2017 and 2020) compared to years of none election (2018 and 2019). There is an overall migration trend from conservatives to progressives during the four years. This change in scores across the four year time frame suggests a unique political cycle exclusive to Donald Trump’s unprecedented presidential term. Our results in a broad sense portray the potential capabilities of a data collection and scoring algorithm that detected a noticeable political migration and describes the broad social characteristics of certain politically aligned users on social media platforms.

We studied international migrations of researchers, scientists, and academics, to better understand the so-called “brain drain” phenomenon, if and how it can be measured, and how it changes over time. We discuss why some trivial measures can be ineffective, and as a consequence, we built the global scientific migration network to identify the most important countries involved in the mobility of scholars, and to study their role at a local and a global scale.

For such a purpose, we analysed a temporal directed weighted network representing scientists moving from one country to another, from 2000 to 2016, built on top of 2.8 million ORCID public profiles. With the support of the well-known HITS algorithm, we found hubs and authorities to study the interplay between providing and attracting researchers from a global perspective, and its relationship to other structural features.

Our findings highlight the presence of a set of countries acting both as hubs and authorities, occupying a privileged position in the Scientific Migration Network, that is network of the scientific migrations, and having similar local characteristics, i.e., several neighbours with highly differentiated flows of researchers moving from/to them. However, it is striking that some of these countries have a predominant role over the others, and that we can easily observe countries that are extremely more attractive than others, as well as other countries that perform better as exporters than importers of scientists. It is also interesting that hubs and authorities scores can change over time, alongside with their relative discrepancy, and other network measures, suggesting that local and/or global policies can buck the trend.

Online firestorms pose a serious threat to companies and cause spontaneous information asymmetry between companies and social media users, which is part of the principal-agent theory. Corporate crisis management has already developed strategies to deal with firestorms, but these strategies are more effective if the company identifies a firestorm at an early stage. Therefore, we first identify literature-based characteristics of firestorms and quantify these using data-driven features in a multiple-case study approach based on Twitter data. Secondly, we identify per case the beginning of the firestorm and days with the least fluctuation in the number of posts as reference days. Finally, we compare the features between the starting points and the reference days to determine which features are significantly different. We could identify 24 features that change significantly at the beginning of a firestorm. This enables us to determine which features a company must pay particular attention to in order to detect a firestorm at an early stage. Likewise, we discuss these features in the context of the principal-agent theory with the use of social synchrony and crowd psychology to show how these features change information diffusion and contribute to information asymmetry.

Social networks such as Facebook, Twitter, Instagram play an important role in information diffusion. To understand how information is diffused in these social networks, it is important to examine users’ online activities and behaviors. In this work, we focus on Twitter and study the impact of users’ behaviors on their retweet activities (the major way of information diffusion on Twitter). We consider the topic preference, emotion and personality of a user as part of the user profile to represent their online behavior. The user profile can be built based on all their past tweets, retweets, or both. We propose two types of retweet prediction models, one is using classification algorithms, and the other is using matrix factorization algorithms. In the matrix factorization approach, we include behavior features into the basic factorization model through newly defined regularization terms. The experimental results show that in terms of the F1-score, our classification models based on user behavior related features provided 5%-9% improvement over baseline models and the matrix factorization model showed 4%-6% improvement over the baseline. We also find that by only considering the retweets, the data processing time is reduced while the prediction accuracy is comparable to the case when both retweets and tweets are included.

Influence propagation in social networks is a subject of growing interest. A relevant issue in those networks involves the identification of key influencers. These players have an important role on viral marketing strategies and message propagation, including political propaganda and fake news. In effect, an important way to fight malicious usage on social networks is to understand their properties, their structure and the way messages propagate.

This paper proposes a new index for analyzing message propagation in social networks, based on the network topological nature and the influential power of the message. The new index characterizes the strength of each node as a launcher of the message, dividing the nodes into launchers and non-launchers. This division is most evident when the viral power of the message is high. Together with other known metrics, launcher individuals can assist to select efficient influencers in a social network. For instance, instead of choosing a strong member according to its degree in the network (number of followers), we may previously select those belonging to the launchers group and then look for the lowest degree members contained therein. These members are probably cheaper (on financial incentives) but still guarantying almost the same influence effectiveness as the largest degree members.

We discuss this index using a number of real-world social networks available in known datasets repositories.