Donations to charity-based crowdfunding environments have been on the rise in the last few years. Unsurprisingly, deception and fraud in such platforms have also increased, but have not been thoroughly studied to understand what characteristics can expose such behavior and allow its automatic detection and blocking. Indeed, crowdfunding platforms are the only ones typically performing oversight for the campaigns launched in each service. However, they are not properly incentivized to combat fraud among users and the campaigns they launch: on the one hand, a platform’s revenue is directly proportional to the number of transactions (since the platform charges a fixed amount per donation); on the other hand, if a platform is transparent with respect to how much fraud it has, it may discourage potential donors from participating.
In this paper, we take the first step in studying fraud in crowdfunding campaigns. We analyze data collected from different crowdfunding platforms, and annotate 700 campaigns as fraud or not. We compute various textual and image-based features and study their distributions and how they associate with campaign fraud. Using these attributes, we build machine learning classifiers, and show that it is possible to automatically classify such fraudulent behavior with up to 90.14% accuracy and 96.01% AUC, only using features available from the campaign’s description at the moment of publication (i.e., with no user or money activity), making our method applicable for real-time operation on a user browser.
Coordinated groups of user accounts working together in online social media can be used to manipulate the online discourse and thus is an important area of study. In this study, we work towards a general theory of coordination. There are many ways to coordinate groups online: semantic, social, referral and many more. Each represents a coordination dimension, where the more dimensions of coordination are present for one event, the stronger the coordination present. We build on existing approaches that detect coordinated groups by identifying high levels of synchronized actions within a specified time window. A key concern with this approach is the selection of the time window. We propose a method that selects the optimal window size to accurately capture local coordination while avoiding the capture of coincidental synchronicity. With this enhanced method of coordination detection, we perform a comparative study across four events: US Elections Primaries 2020, Reopen America 2020, Capitol Riots 2021 and COVID Vaccine Release 2021. Herein, we explore the following three dimensions of coordination for each event – semantic, referral and social coordination – and perform group and user analysis within and among the events. This allows us to expose different user coordination behavior patterns and identify narratives and user support themes, hence estimating the degree and theme of coordination.
User interest tracing is a common practice in many Web use-cases including, but not limited to, search, recommendation or intelligent assistants. The overall aim is to provide the user a personalized “Web experience” by aggregating and exploiting a plenitude of user data derived from collected logs, accessed contents, and/or mined community context. As such, fairly basic features such as terms and graph structures can be utilized in order to model a user’s interest. While there are clearly positive aspects in the before mentioned application scenarios, the user’s privacy is highly at risk. In order to highlight inherent privacy risks, this paper studies Semantic User Interest Tracing (SUIT in short) by investigating a user’s publishing/editing behavior of Web contents. In contrast to existing approaches, SUIT solely exploits the (semantic) concepts [categories] inherent in documents derived via entity-level analytics. By doing so, we raise Web contents to the entity-level. Thus, we are able to abstract the user interest from plain text strings to “things”. In particular, we utilize the inherited structural relationships present among the concepts derived from a knowledge graph in order to identify the user associated with a specific Web content. Our extensive experiments on Wikipedia show that our approach outperforms state of the art approaches in tracing and predicting user behavior in a single language. In addition, we also demonstrate the viability of our semantic (language-agnostic) approach in multi-lingual experiments. As such, SUIT is capable of revealing the user’s identity, which demonstrates the fine line between personalization and surveillance, raising questions regarding ethical considerations at the same time.
Our lives are ruled by events of varying importance ranging from simple everyday occurrences to incidents of societal dimension. And a lot of effort is taken to exchange information and discuss about such events: generally speaking, stringent narratives are formed to reduce complexity. But when considering complex events like the current conflict between Russia and Ukraine it is easy to see that those events cannot be grasped by objective facts alone, like the start of the conflict or respective troop sizes. There are different viewpoints and assessments to consider, a different understanding of the roles taken by individual participants, etc. So how can such subjective and viewpoint-dependent information be effectively represented together with all objective information? Recently event-centric knowledge graphs have been proposed for objective event representation in the otherwise primarily entity-centric domain of knowledge graphs. In this paper we introduce a novel and lightweight structure for event-centric knowledge graphs, which for the first time allows for queries incorporating viewpoint-dependent and narrative aspects. Our experiments prove the effective incorporation of subjective attributions for event participants and show the benefits of specifically tailored indexes for narrative query processing.
In recent years, governments worldwide have moved their services online to better serve their citizens. Benefits aside, this choice increases the danger of tracking via such sites. This is of great concern as governmental websites increasingly become the only interaction point with the government. In this paper, we investigate popular governmental websites across different countries and assess to what extent the visits to these sites are tracked by third-parties. Our results show that, unfortunately, tracking is a serious concern, as in some countries up to 90% of these websites create cookies of third-party trackers without any consent from users. Non-session cookies, that are created by trackers and can last for days or months, are widely present even in countries with strict user privacy laws. We also show that the above is a problem for official websites of international organizations and popular websites that inform the public about the COVID-19 pandemic.
Online social media works as a source of various valuable and actionable information during disasters. These information might be available in multiple languages due to the nature of user generated content. An effective system to automatically identify and categorize these actionable information should be capable to handle multiple languages and under limited supervision. However, existing works mostly focus on English language only with the assumption that sufficient labeled data is available. To overcome these challenges, we propose a multilingual disaster related text classification system which is capable to work undervmonolingual, cross-lingual and multilingual lingual scenarios and under limited supervision. Our end-to-end trainable framework combines the versatility of graph neural networks, by applying over the corpus, with the power of transformer based large language models, over examples, with the help of cross-attention between the two. We evaluate our framework over total nine English, Non-English and monolingual datasets invmonolingual, cross-lingual and multilingual lingual classification scenarios. Our framework outperforms state-of-the-art models in disaster domain and multilingual BERT baseline in terms of Weighted F1 score. We also show the generalizability of the proposed model under limited supervision.
Synchronous, face-to-face interactions such as brainstorming are considered essential for creative tasks (the old normal). However, face-to-face interactions are difficult to arrange because of the diverse locations and conflicting availability of people—a challenge made more prominent by work-from-home practices during the COVID-19 pandemic (the new normal). In addition, face-to-face interactions are susceptible to cognitive interference.
We employ crowdsourcing as an avenue to investigate creativity in asynchronous, online interactions. We choose product ideation, a natural task for the crowd since it requires human insight and creativity into what product features would be novel and useful. We compare the performance of solo crowd workers with asynchronous teams of crowd workers formed without prior coordination. Our findings suggest that, first, crowd teamwork yields fewer but more creative ideas than solo crowdwork. The enhanced team creativity results when Second, cognitive interference, known to inhibit creativity in face-to-face teams, may not be significant in crowd teams. Third, teamwork promotes better achievement emotions for crowd workers. These findings provide a basis for trading off creativity, quantity, and worker happiness in setting up crowdsourcing workflows for product ideation.
The slow increasing rate of representation of women and other minorities, particularly in Information Technology (IT) companies, suggests that the current recruiting strategies for attracting underrepresented minorities (URMs) may not be effective. While much can be done to improve hiring strategies, little attention has been paid to how job seekers’ perceptions (positive or otherwise) of a company may affect its attractiveness. For instance, newer generations of professionals are more likely to avoid companies that are not committed to diversity causes. Studies have in fact shown that job seekers increasingly rely on social media to inform themselves about targeted companies. In this paper, we investigate the “social-mediascape” of Black Tech communities and IT companies, namely, the spaces of interactions and communications and their influences on individuals’ perceptions of particular companies. To this end, we look into the Twitter Black and Tech communities in Brazil and in the United States. We rely on “Twitter lists” that are curated by the users of the platform and effective in capturing topical homophily. A research challenge in itself, we provide the first large-scale compositions of Black Tech communities and their connection to IT companies based on followership data on Twitter. After analyzing these compositions, we can then create perceptual maps between communities and IT companies. Our results suggest that there is a stronger correlation between Black activism and technology in the US context. For the Brazilian context, we found a stronger correlation between the Black Tech and general Software Developer users communities suggesting that both racial activism and technology are important topics to attract the Brazilian Black Tech community’s interests.
In 2020, amidst the COVID pandemic and a polarized political climate, the Sleeping Giants online activist movement gained traction in Brazil. Its rationale was simple: to curb the spread of disinformation by harming the advertising revenue of sources that produce this type of content. Like its international counterparts, Sleeping Giants Brasil (SGBR) campaigned against media outlets using Twitter to ask companies to remove ads from the targeted outlets. This work presents a thorough quantitative characterization of this activism model, analyzing the three campaigns carried out by SGBR between May and September 2020. To do so, we use digital traces from both Twitter and Google Trends, toxicity and sentiment classifiers trained for the Portuguese language, and an annotated corpus of SGBR’s tweets. Our key findings were threefold. First, we found that SGBR’s requests to companies were largely successful (with 83.85% of all 192 targeted companies responding positively) and that user pressure was correlated to the speed of companies’ responses. Second, there were no significant changes in the online attention and the user engagement going towards the targeted media outlets in the six months that followed SGBR’s campaign (as measured by Google Trends and Twitter engagement). Third, we observed that user interactions with companies changed only transiently, even if the companies did not respond to SGBR’s request. Overall, our results paint a nuanced portrait of internet activism. On the one hand, they suggest that SGBR was successful in getting companies to boycott specific media outlets, which may have harmed their advertisement revenue stream. On the other hand, they also suggest that the activist movement did not impact the online popularity of these media outlets nor the online image of companies that did not respond positively to its requests.
Human annotations can help indexing digital resources as well as improving search and recommendation systems. Human annotators may carry their bias and stereotypes in the labels they create when annotating digital content. These are then reflected in machine learning models trained with such data. The result is a reinforcement loop where end-users are pushed stereotypical content by the search and recommendation systems they use on a daily basis. In order to break the loop, the impact on models of using diverse data that can better represent a diverse population has been looked at.
In this work, we look at how human annotators in the US annotate digital content different from content which is popular on the Web and social media. We present the results of a controlled user study in which participants are asked to annotate videos of common tasks performed by people from various socio-economic backgrounds around the world. We test for the presence of social stereotypes and investigate the diversity of the provided annotations, especially since some abstract labels may reveal information about annotators’ emotional state and judgment. We observe different types of annotations for content from different socio-economic levels. Furthermore, we find regional and income level biases in annotation sentiment.
#MyBodyMyChoice is a well-known hashtag originally created to advocate for women’s rights, often used in discourse about abortion and bodily autonomy. The Covid-19 outbreak prompted governments to take containment measures such as vaccination campaigns and mask mandates. Population groups opposed to such measures started to use the slogan “My Body My Choice” to claim their bodily autonomy. In this paper, we investigate whether the discourse around the hashtag #MyBodyMyChoice on Twitter changed its usage after the Covid-19 outbreak. We observe that the conversation around the hashtag changed in two ways. First, semantically, the hashtag #MyBodyMyChoice drifted towards conversations around Covid-19, especially in messages opposed to containment measures. Second, while before the pandemic users used to share content produced by experts and authorities, after Covid-19 the users’ attention has shifted towards individuals.
Obtaining high-quality results for a fixed set of classification tasks with a limited budget is a critical issue in crowdsourcing. The introduction of AI models to complement the process should be explored. However, there are few existing approaches to directly address the problem, which have been proposed in the context of how to train AI models using noisy crowdsourced data. This paper presents a more direct approach for solving the problem of introducing AI to improve the task results of human workers for a fixed number of tasks with a limited budget; we deal with an AI model as a worker and aggregates the results of both human and AI workers in a symmetric manner. The proposed “Human-AI EM” (HAEM) algorithm, which extends the Dawid Skene model, treats the AI models as workers and explicitly computes their confusion matrices to derive higher-quality aggregation results. We conducted an extensive set of experiments and compared HAEM with two other methods (MBEM and Dawid Skene model). We found that AI-powered HAEM showed better performance than the two methods in most cases and that the HAEM often performed better than the Dawid Skene model with additional human workers. We also found that AI workers work well when they are good at identifying particular classes even if they do not have very good overall accuracy.
Achievement systems have been actively adopted in gaming platforms to maintain players’ interests. Among them, trophies in PlayStation games are one of the most successful achievement systems. While the importance of trophy design has been casually discussed in many game developers’ forums, there has been no systematic study of the historical dataset of trophies yet. In this work, we construct a complete dataset of PlayStation games and their trophies and investigate them from both the developers’ and players’ perspectives.
Social norms, the shared informal rules of acceptable behavior, drive and reflect the evolution of societies. As an increasingly large part of social interactions happens online, social media data offers an unprecedented opportunity to assess the perception of social norm boundaries in-the-wild. In this regard, Reddit’s r/AITA represents an invaluable source of codified social norms. This subreddit is an online forum where individuals describe how they acted in a specific situation in the past, and ask for the feedback of the community about whether their behavior was deviant or socially acceptable. Other users in the community share their views and express a judgment codified by a tag.
This study focuses on assessing which factors are associated with judgements expressed by the community. Specifically, we investigate two main factors: the demographics of the author of the submission and the topic of the submission. Our analysis shows a clear gender imbalance in the judgements, with submissions by male authors receiving negative judgements with a 62% higher likelihood. Older authors (≥ 28) also have a higher chance of receiving negative judgements (). Regarding topics, submissions about romantic relationships and work tend to be judged more positively ( and , respectively), thus hinting towards a role of the community as a support group, especially for female participants. We then focus on controversial submissions which garner heterogeneous judgements. We find that these submissions are clearly separable from those ones that are unanimously judged, and that male and older (≥ 28) authors are more likely to describe controversial situations that split the community ( and , respectively).
Finally, we focus on the characteristics of the evaluators. We find that their judgements are associated with the other communities they belong to (signifying other interests and experiences), with an effect size comparable to the demographic group of the author. By combining all these variables—demographics of the author and communities of the evaluator—we are able to build a classifier that predicts a deviance judgement on a submission with AUC = 0.85.
Team mining is concerned with the identification of a group of experts that are able to collaborate with each other in order to collectively cover a set of required skills. This problem has mainly been addressed either through graph search, which looks for subgraphs that satisfy the skill requirements or through neural architectures that learn a mapping from the skill space to the expert space. An exact graph-based solution to this problem is intractable and its heuristic variants are only able to identify sub-optimal solutions. On the other hand, neural architecture-based solutions are prone to overfitting and simplistically reduce the problem of team formation to one of expert ranking. Our work in this paper proposes an unsupervised heterogeneous skip-gram-based subgraph mining approach that can learn representations for subgraphs in a collaboration network. Unlike previous work, the subgraph representations allow our method to mine teams that have past collaborative history and collectively cover the requested desirable skills. Through our experiments, we demonstrate that our proposed approach is able to outperform a host of state-of-the-art team mining techniques from both quantitative and qualitative perspectives.
The recent advances in natural language generation provide an additional tool to manipulate public opinion on social media. Even though there has not been any report of malicious exploit of the newest generative techniques so far, disturbing human-like scholarly examples of GPT-2 and GPT-3 can be found on social media. Therefore, our paper investigates how the state-of-the-art deepfake social media text detectors perform at recognizing GPT-2 tweets as machine-written, also trying to improve the state-of-the-art by hyper-parameter tuning and ensembling the most promising detectors; finally, our work concentrates on studying the detectors’ capabilities to generalize over tweets generated by the more sophisticated and complex evolution of GPT-2, that is GPT-3. Results demonstrate that hyper-parameter optimization and ensembling advance the state-of-the-art, especially on the detection of GPT-2 tweets. However, all tested detectors dramatically decreased their accuracy on GPT-3 tweets. Despite this, we found out that even though GPT-3 tweets are much closer to human-written tweets than the ones produced by GPT-2, they still have latent features in common share with other generative techniques like GPT-2, RNN and other older methods. All things considered, the research community should quickly devise methods to detect GPT-3 social media texts, as well as older generative methods.
Short videos have become one of the leading media used by younger generations to express themselves online and thus a driving force in shaping online culture. In this context, TikTok has emerged as a platform where viral videos are often posted first. In this paper, we study what elements of short videos posted on TikTok contribute to their virality. We apply a mixed-method approach to develop a codebook and identify important virality features. We do so vis-à-vis three research hypotheses; namely, that: 1) the video content, 2) TikTok’s recommendation algorithm, and 3) the popularity of the video creator contributes to virality.
We collect and label a dataset of 400 TikTok videos and train classifiers to help us identify the features that influence virality the most. While the number of followers is the most powerful predictor, close-up and medium-shot scales also play an essential role. So does the lifespan of the video, the presence of text, and the point of view. Our research highlights the characteristics that distinguish viral from non-viral TikTok videos, laying the groundwork for developing additional approaches to create more engaging online content and proactively identify possibly risky content that is likely to reach a large audience.
Nowadays, Online Social Media (OSM) are among the most popular web services. Traditional OSM are known to be affected by serious issues including misinformation, fake news, censorship, and privacy violations, to the point that a pressing demand for new paradigms is raised by users all over the world. Among such paradigms, the concepts around the Web 3.0 are fueling a new revolution of online sociality, pushing towards the adoption of innovative and groundbreaking technologies. In particular, the decentralization of social services through the blockchain technology is representing the most valid alternative to current OSM, enabling the development of rewarding strategies for value redistribution, and fake news detection. However, the so-called Blockchain Online Social Media (BOSMs) are far from being mature, with different platforms that continually try to redefine their services in order to attract larger audiences, thus causing blockchain forks and massive user migrations, with the latter dominating the dynamics of the current OSM landscape, too.
In this paper, we deal with the evolution of BOSMs from the perspective of user migration across platforms as a consequence of a fork event. We propose a general user migration model applicable to BOSMs to represent the evolution patterns of fork-based migrations, the multi-interaction structural complexity of BOSMs, and their growth characteristics. Within this framework, we also cope with the task of predicting how users will behave in the case of a fork, i.e. they will remain on the original blockchain or they will migrate to the new one. We apply our framework to the case study of the Steem-Hive fork event, and show the importance of considering both social and economic information, regardless of the learning algorithm considered. To the best of our knowledge, this is the first study on blockchain fork and its related user migration.
Missing children, i.e., children reported to a relevant authority as having “disappeared,” constitute an important but often overlooked population. From a research perspective, missing children constitute a hard-to-reach population about which little is known. This is a particular problem in regions of the Global South that lack robust or centralized data collection systems. In this study, we analyze the composition of the population of missing children in Guatemala, a country with high levels of violence. We contrast the official aggregated-level data from the Guatemalan National Police during the 2018-2020 period with real-time individual-level data on missing children from the official Twitter account of the Alerta Alba-Keneth, a governmental warning system tasked with disseminating information about missing children. Using the Twitter data, we characterize the population of missing children in Guatemala by single-year age, sex, and place of disappearance. Our results show that women are more likely to be reported as missing, particularly those aged 13-17. We discuss the findings in light of the known links between missing people, violence, and human trafficking. Finally, the study highlights the potential of web data to contribute to society by improving our understanding of this and similar hard-to-reach populations.
Large-scale manipulations on social media have two important characteristics: (i) they use propaganda to influence others, and (ii) they adopt coordinated behavior to spread propaganda and to amplify its impact. Despite the connection between them, these two characteristics have so far been considered in isolation. Here we aim to bridge this gap. In particular, we analyze the spread of propaganda and its interplay with coordinated behavior on a large Twitter dataset about the 2019 UK general election. We first propose and evaluate several measures for quantifying the use of propaganda on Twitter. Then, we investigate the use of propaganda by different coordinated communities that participated in the online debate. The combined analysis of propaganda and of coordination provides evidence about the harmfulness of coordinated communities that would not be available otherwise. For instance, it allows us to identify a harmful politically-oriented community as well as a harmless community of grassroots activists. Finally, we compare our measures of propaganda and of coordination to automation scores (i.e., the use of bots) and Twitter suspensions, revealing interesting trends. From a theoretical viewpoint, we introduce a methodology for analyzing several important dimensions of online behavior that are seldom conjointly considered. From a practical viewpoint, we provide new and nuanced insights into inauthentic and harmful online activities in the run up to the 2019 UK general election.
Using data from a major international news organization, we investigate the effect of hiding the count of dislikes from YouTube viewers on the propensity to use the video like/dislike features. We compare one entire month of videos before (n = 478) and after (n = 394) YouTube began hiding the dislikes counts. Collectively, these videos had received 450,200 likes and 41,892 dislikes. To account for content variability, we analyze the likes/dislikes by sentiment class (positive, neutral, negative). Results of chi-square testing show that while both likes and dislikes decreased after the hiding, dislikes decreased substantially more. We repeat the analysis with four other YouTube news channels in various languages (Arabic, English, French, Spanish) and one non-news organization, with similar results in all but one case. Findings from these multiple organizations suggest that YouTube hiding the number of dislikes from viewers has altered the user-platform interactions for the like/dislike features. Therefore, comparing the like/dislike metrics before and after the removal would give invalid insights into users’ reactions to content on YouTube.
Social media allows researchers to track societal and cultural changes over time based on language analysis tools. Many of these tools rely on statistical algorithms which need to be tuned to specific types of language. Recent studies have shown the absence of appropriate tuning, specifically in the presence of semantic shift, can hinder robustness of the underlying methods. However, little is known about the practical effect this sensitivity may have on downstream longitudinal analyses. We explore this gap in the literature through a timely case study: understanding shifts in depression during the course of the COVID-19 pandemic. We find that inclusion of only a small number of semantically-unstable features can promote significant changes in longitudinal estimates of our target outcome. At the same time, we demonstrate that a recently-introduced method for measuring semantic shift may be used to proactively identify failure points of language-based models and, in turn, improve predictive generalization.
Despite their important role in online information search, search query suggestions have not been researched as much as most other aspects of search engines. Although reasons for this are multi-faceted, the sparseness of context and the limited data basis of up to ten suggestions per search query pose the most significant problem in identifying bias in search query suggestions. The most proven method to reduce sparseness and improve the validity of bias identification of search query suggestions so far is to consider suggestions from subsequent searches over time for the same query. This work presents a new, alternative approach to search query bias identification that includes less high-level suggestions to deepen the data basis of bias analyses. We employ recursive algorithm interrogation techniques and create suggestion trees that enable access to more subliminal search query suggestions. Based on these suggestions, we investigate topical group bias in person-related searches in the political domain.
Network-based people recommendation algorithms are widely employed on the Web to suggest new connections in social media or professional platforms. While such recommendations bring people together, the feedback loop between the algorithms and the changes in network structure may exacerbate social biases. These biases include rich-get-richer effects, filter bubbles, and polarization. However, social networks are diverse complex systems and recommendations may affect them differently, depending on their structural properties. In this work, we explore five people recommendation algorithms by systematically applying them over time to different synthetic networks. In particular, we measure to what extent these recommendations change the structure of bi-populated networks and show how these changes affect the minority group.
Our systematic experimentation helps to better understand when link recommendation algorithms are beneficial or harmful to minority groups in social networks. In particular, our findings suggest that, while all algorithms tend to close triangles and increase cohesion, all algorithms except Node2Vec are prone to favor and suggest nodes with high in-degree. Furthermore, we found that, especially when both classes are heterophilic, recommendation algorithms can reduce the visibility of minorities.
The misuse of social media platforms to influence public opinion through propaganda campaigns are a cause of rising concern globally. Particularly, countries like India, where politicians communicate with the public through unmediated, curated twitter feeds, have witnessed a significant surge in strategic online manipulation. In this paper, we study propaganda messaging on Indian Twitter during two politically polarizing events. We collect over 80M Hindi and English tweets from over 26k politicians and 6k influencers. Using a mixed-methods approach, we identify major propaganda narratives across all events. We further use a network causal inference based approach to isolate influential actors who play a significant role in propagating the identified narratives. We conclude by discussing how these opinion leaders and their information dissemination, are central to instigating and building propaganda campaigns on Twitter.
With increased recent awareness on the possible impact of retrieval techniques on intensifying gender biases, researchers have embarked on defining quantifiable gender bias metrics that can provide the means to concretely measure such biases in practice. While successful in allowing for identifying possible sources of gender bias, there has been little work that systematically explores the characteristics of these metrics. This paper argues that effective future works on gender biases in information retrieval require a careful understanding of the bias metrics in terms of their consistency, robustness, sensitivity and also their relation with psychological characteristics and what they actually measure. Through our experiments, we show that more rigorous work on gender bias metrics need to be pursued as existing metrics may not necessarily be consistent and robust and often capture differing psychological characteristics.
In this study, we characterize the cross-platform mobilization of YouTube and BitChute videos on Twitter during the 2020 U.S. Election fraud discussions. Specifically, we extend the VoterFraud2020 dataset  to describe the prevalence of content supplied by both platforms, the mobilizers of that content, the suppliers of that content, and the content itself. We find that while BitChute videos promoting election fraud claims were linked to and engaged with in the Twitter discussion, they played a relatively small role compared to YouTube videos promoting fraud claims. This core finding points to the continued need for proactive, consistent, and collaborative content moderation solutions rather than the reactive and inconsistent solutions currently being used. Additionally, we find that cross-platform disinformation spread from video platforms was not prominently from bot accounts or political elites, but rather average Twitter users. This finding supports past work arguing that research on disinformation should move beyond a focus on bots and trolls to a focus on participatory disinformation spread.
This paper examines news consumption in response to a major polarizing event, and we use the May 2021 Israeli-Palestinian conflict as an example. We conduct a detailed analysis of the news consumption of more than eight thousand Twitter users who are either pro-Palestinian or pro-Israeli and authored more than 29 million tweets between January 1 and August 17, 2021. We identified the stance of users using unsupervised stance detection. We observe that users may consume more topically-related content from foreign and less popular sources, because, unlike popular sources, they may reaffirm their views, offer more extreme, hyper-partisan, or sensational content, or provide more in depth coverage of the event. The sudden popularity of such sources may not translate to longer-term or general popularity on other topics.
The growing political polarization of the American electorate over the last several decades has been widely studied and documented. During the administration of President Donald Trump, charges of “fake news” made social and news media not only the means but, to an unprecedented extent, the topic of political communication. This extreme political polarization continued through the election and all through the period up to the attempted takeover of the Capitol on January 6, 2021. In this paper, we analyze this tumultuous phase in American history through the lens of news viewership. We consider the official YouTube channels of six US cable news networks across a wide political spectrum with a specific focus on three conservative fringe news networks. We analyze how the viewers reacted to the different ways the election outcome was covered by these news outlets. This paper makes two distinct types of contributions. The first is to introduce a novel methodology to analyze large social media data to study the dynamics of US news networks and their viewers. The second is to provide insights into what actually happened regarding these news networks and their viewerships during this volatile 64 day period. Our empirical evidence suggest that recent natural language processing advancements can be harnessed in a synergistic way to mine political insights from large scale social media data.
Online social media platforms have evolved into a significant place for debate around socio-political phenomena such as government policies and bills. Studying online debates on such topics can help infer people’s perception and acceptance of the happenings. At the same time, various inauthentic users that often pollute the democratic discussion of the subject need to be weeded out from the debate. The characterization of a campaign keeping in mind various forms of involved actors thus becomes very important. On December 12, 2019, Citizenship Amendment Act (CAA) was enacted by the Indian Government, triggering a debate on whether the act was unfair. In this work, we investigate the user’s perception of the #CitizenshipAmendmentAct on Twitter, as the campaign unrolled with divergent discourse in the country. Keeping the campaign participants as the prime focus, we study 9,947,814 tweets produced by 275,111 users during the starting 3 months of protest. Our study includes the analysis of user engagement, content, and network properties with online accounts divided into authentic (genuine users) and inauthentic (bots, suspended, and deleted) users. Our findings show different themes in shared tweets among protesters and counter-protesters. We find presence of inauthentic users on both side of discourse, with counter-protesters having more inauthentic users than protesters. The follow network of the users suggests homophily among users on the same side of discourse and connection between various inauthentic and authentic users. This work contributes to filling the gap of understanding the role of users (from both sides) in a less studied geo-location, India.
After George Floyd’s death in May 2020, the volume of discussion in social media increased dramatically. A series of protests followed this tragic event, called as the 2020 BlackLivesMatter movement. Eventually, many user accounts are deleted by their owners or suspended due to violating the rules of social media platforms. In this study, we analyze what happened in Twitter before and after the event triggers with respect to deleted and suspended users. We create a novel dataset that includes approximately 500k users sharing 20m tweets, half of whom actively participated in the 2020 BlackLivesMatter discussion, but some of them were deleted or suspended later. We particularly examine the factors for undesirable behavior in terms of spamming, negative language, hate speech, and misinformation spread. We find that the users who participated to the 2020 BlackLivesMatter discussion have more negative and undesirable tweets, compared to the users who did not. Furthermore, the number of new accounts in Twitter increased significantly after the trigger event occurred, yet new users are more oriented to have undesirable tweets, compared to old ones.
Social networking sites like Twitter and Facebook have become dominant sources of political activity with many politicians choosing to leverage these platforms. This rise in popularity has led many researchers to investigate the impact of these platforms on digital communication, specifically with regard to digital polarization. Most of the current polarization literature is centered on two-party systems with little attention given to multi-party parliamentary networks. As such, we leverage data from members of the German Bundestag (MdBs) to better understand digital polarization in MdB Twitter interactions. This paper expands the current literature to include a multi-level network evaluation of a parliamentary body by evaluating partisan polarization on the scale of retweets, mentions, and following-follower relationships. We employ social network analysis and text-based sentiment analysis to understand both the structure and sentiment polarity of the networks and find that polarization varies based on the level of the network that is being examined. Results indicate that the highest polarization occurs in the retweets network and the lowest occurs in the mentions network, while the sentiment analysis suggests same-party mentions are significantly more positive than cross-party mentions.
The Web contains several social media platforms for exchange of ideas and content publishing. These platforms are used by people, but also by distributed agents known as bots. Bots have existed for decades, with many of them being benevolent, although their influence in propagating and generating deceptive information has increased recently. Here we present a characterization of the discussion on Twitter about the 2020 Chilean constitutional referendum. Through a profile-oriented analysis that enables the isolation of anomalous content using machine learning, we obtain a characterization that matches national vote turnout, and we measure how anomalous accounts (some of which are automated bots) produce content and interact promoting (false) information.
The way people respond to messaging from public health organizations on social media can provide insight into public perceptions on critical health issues, especially during a global crisis such as COVID-19. It could be valuable for high-impact organizations such as the US Centers for Disease Control and Prevention (CDC) or the World Health Organization (WHO) to understand how these perceptions impact reception of messaging on health policy recommendations. We collect two datasets of public health messages and their responses from Twitter relating to COVID-19 and Vaccines, and introduce a predictive method which can be used to explore the potential reception of such messages. Specifically, we harness a generative model (GPT-2) to directly predict probable future responses and demonstrate how it can be used to optimize expected reception of important health guidance. Finally, we introduce a novel evaluation scheme with extensive statistical testing which allows us to conclude that our models capture the semantics and sentiment found in actual public health responses.
Over the past several years, a growing number of social media platforms have begun taking an active role in content moderation and online speech regulation. While enforcement actions have been shown to improve outcomes within moderating platforms, less is known about possible spillover effects across platforms. We study the impact of removing groups promoting anti-vaccine content on Facebook on engagement with similar content on Twitter. We followed 160 Facebook groups discussing COVID-19 vaccines and prospectively tracked their removal from the platform between April and September 2021. We then identified users who cited these groups on Twitter, and examined their online behavior over time. Our findings from a stacked difference-in-differences analysis shows that users citing removed Facebook groups promoted more anti-vaccine content on Twitter in the month after the removals. In particular, users citing the removed groups used 10-33% more anti-vaccine keywords on Twitter, when compared to accounts citing groups that were not removed. Our results suggest that taking down anti-vaccine content on one platform can result in increased production of similar content on other platforms, raising questions about the overall effectiveness of these measures.
Social media data such as tweets have been seen as a convenient source of information to enhance situational awareness, and to assist local governments in decision making and response actions in crisis. However, extracting the relevant information for different types of situational awareness has been challenging. Existing studies have investigated classifications of crisis information on social media, but not much focus has been put on the classification of pandemic related information. Pandemics are public health related crisis that present unique characteristics. We propose to classify pandemic tweets from three perspectives, i.e., informativeness, geographic view, and information source, after a comprehensive analysis of the factors determining the relevance of information to situational awareness. The joint use of three-faceted classifications will enable the identification of relevant data for multiple purposes of situational awareness. We manually annotate a dataset with COVID-19 tweets and explore multi-task learning models for the classification of three tasks simultaneously. The proposed multi-task neural network models show improved performance compared with single learning models. We also find that pretraining multi-task models with relevant crisis datasets can further boost the performance. Specifically, multi-task models can significantly increase the recall of ‘informative’ and ‘local’ tweets, which are important for local response actions and policy decision making.
The spread of online misinformation has become a major global risk. Understanding how misinformation propagates on social media is vital. While prior studies suggest that the content factors, such as emotion and topic in texts, are closely related to the dissemination of misinformation, the effect of users’ commentary on misinformation during its spreading on social media has been long overlooked. In this paper, we identify the patterns of “misinformation mutation” which captures ways misinformation is commented and shared by social media users. Our study focus on misinformation originated from digital news outlets and shared on Twitter. Through an analysis of over 240 thousand tweets capturing how users share COVID-19 pandemic-related misinformation news over a five-month period, we study the prevalence and factors of the misinformation mutation. We examine the different kinds of mutation in terms of how the article was cited from the news source, and how the content was edited, compared with its original text, and test the relationship between misinformation’s mutation and its spread on Twitter. Our results indicate a positive relationship between information mutation and spreading outcome – and such a relationship is stronger for news articles shared from non-credible outlets than those from credible ones. This study provides the first quantitative evidence of how misinformation propagation may be exacerbated by users’ commentary. Our study contributes to the understanding of misinformation spreading on social media and has implications for countering misinformation.
Shortly after the outbreak of the novel coronavirus disease (Covid-19), the United Nations declared an infodemic due to an unprecedented amount of false information spreading about Covid-19. A study made by the center for countering digital hate found out that twelve individuals, referred to as Disinformation Dozen (Disinfo12), were responsible for 65% of Covid-19 misinformation circulating on social media. Given the Disinfo12’s detrimental impact in spreading misinformation, in this work, we perform an exploratory analysis on Disinfo12’s activity on Twitter aiming at identifying their sharing strategies, favorite sources of information, and potential secondary actors contributing to the proliferation of questionable narratives. In our study, we uncovered the distinctive facets that allowed Disinfo12 to act as primary sources of information, and we recognized that YouTube represent one of the favorite information sources to spread questionable narratives and conspiracy theories. Finally, we recognized that right-leaning accounts are embedded in Disinfo12’s community and represent the main spreaders of content generated by the Disinformation Dozen.
Homeopathy is a medical system originating in Germany more than 200 years ago. Based on prior investigations, mainstream health agencies and medical research communities indicate that there is little evidence that homeopathy can be an effective treatment for any specific health condition. However, it continues to be practiced as a popular form of alternative medicine in many countries, even during the ongoing COVID-19 pandemic. In this paper, we mine opinions on homeopathy for COVID-19 expressed in Twitter data. Our experiments are conducted with a dataset of nearly 60K tweets collected during a seven month period ending in July 2020. We first built text classifiers (linear and neural models) to mine opinions on homeopathy (positive, negative, neutral) from tweets using a dataset of 2400 hand-labeled tweets obtaining an average macro F-score of 81.5% for the positive and negative classes. We applied this model to identify opinions from the full dataset. Our results show that the number of unique positive tweets is twice that of the number of unique negative tweets; but when including retweets, there are 23% more negative tweets overall indicating that negative tweets are getting more retweets and better traction on Twitter. Using a word shift graph analysis on the Twitter bios of authors of positive and negative tweets, we observe that opinions on homeopathy appear to be correlated with political/religious ideologies of the authors (e.g., liberal vs nationalist, atheist vs Hindu). To our knowledge, this is the first study to analyze public opinions on homeopathy on any social media platform. Our results surface a tricky landscape for public health agencies as they promote evidence-based therapies and preventative measures for COVID-19.
Content Warning: This paper shows examples of explicit and offensive language that might be triggering. The main goal of this paper is to investigate the temporal shifts of attitudes about racism towards Asians using online social media posts. An increase in race-related crimes towards Asian communities during COVID-19 is the prime motivation of the work presented in this paper. Towards this goal, social media data from Reddit that were shared between January 2018 and December 2021 are utilized to examine the user attitudes towards anti-Asian hate along with the temporal shifts in those attitudes. Using natural language processing techniques, this study conducts a quantitative investigation on how linguistic emotions are expressed in user posts as well as the engagement received by such posts. Our data suggest that anti-Asian hate crimes or incidents have been discussed long before COVID-19, but it is during the pandemic when we noticed a sharp increase in the number of posts about this topic. Psycholinguistic emotions highlight the surge of posts from users sharing about their personal experiences during the pandemic. Topics extracted show the shift of discussions from relating anti-Asian hate crimes to different types of phobias to issues that are more related to personal encounters or private lives/opinions of users during COVID-19. Our work used “score” as a metric to measure online support, which shows an increase of support to users and an increased agreement with the posts the users are sharing on Reddit during the pandemic.
YouTube is one of the most popular social media and online video sharing platforms, and users turn to it for entertainment by consuming music videos, for educational or political purposes, advertising, etc. In the last years, hundreds of new channels have been creating and sharing videos targeting children, with themes related to animation, superhero movies, comics, etc. Unfortunately, many of these videos have been found to be inappropriate for consumption by their target audience, due to disturbing, violent, or sexual scenes.
In this paper, we study YouTube channels that were found to post suitable or disturbing videos targeting kids in the past. Unfortunately, we identify a clear discrepancy between what YouTube assumes and flags as inappropriate content and channel, vs. what is found to be disturbing content and still available on the platform, targeting kids. In particular, we find that almost 60% of videos that were manually annotated and classified as disturbing by an older study in 2019 (a collection bootstrapped with Elsa and other keywords related to children videos), are still available on YouTube in mid 2021. In the meantime, 44% of channels that uploaded such disturbing videos, have yet to be suspended and their videos to be removed. For the first time in literature, we also study the “madeForKids” flag, a new feature that YouTube introduced in the end of 2019, and compare its application to the channels that shared disturbing videos, as flagged from the previous study. Apparently, these channels are less likely to be set as “madeForKids” than those sharing suitable content. In addition, channels posting disturbing videos utilize their channel features such as keywords, description, topics, posts, etc., in a way that they appeal to kids (e.g., using game-related keywords). Finally, we use a collection of such channel and content features to train machine learning classifiers that are able to detect, at channel creation time, when a channel will be related to disturbing content uploads. These classifiers can help YouTube content moderators reduce such incidences, by pointing to potentially suspicious accounts, without analyzing actual videos, but instead only using channel characteristics.
Facebook has recently launched the hateful meme detection challenge, which garnered much attention in academic and industry research communities. Researchers have proposed multimodal deep learning classification methods to perform hateful meme detection. While the proposed methods have yielded promising results, these classification methods are mostly supervised and heavily rely on labeled data that are not always available in the real-world setting. Therefore, this paper explores and aims to perform hateful meme detection in a zero-shot setting. Working towards this goal, we propose Target-Aware Multimodal Enhancement (TAME), which is a novel deep generative framework that can improve existing hateful meme classification models’ performance in detecting unseen types of hateful memes. We conduct extensive experiments on the Facebook hateful meme dataset, and the results show that TAME can significantly improve the state-of-the-art hateful meme classification methods’ performance in seen and unseen settings.
With the rise in accessibility and popularity of various social media platforms, people have started expressing and communicating their ideas, opinions, and interests online. While these platforms are active sources of entertainment and idea-sharing, they also attract hostile and offensive content equally. Identification of hostile posts is an essential and challenging task. In particular, Hindi-English Code-Mixed online posts of conversational nature (which have a hierarchy of posts, comments, and replies) have escalated the challenges. There are two major challenges: (1) the complex structure of Code-Mixed text and (2) filtering the relevant previous context for a given utterance. To overcome these challenges, in this paper, we propose a novel hierarchical neural network architecture to identify hostile posts/comments/replies in online Hindi-English Code-Mixed conversations. We leverage large multilingual pre-trained (mLPT) models like mBERT, XLMR, and MuRIL. The mLPT models provide a rich representation of code-mix text and hierarchical modeling leads to a natural abstraction and selection of the relevant context. The propose model consistently outperformed all the baselines and emerged as a state-of-the-art performing model. We conducted multiple analyses and ablation studies to prove the robustness of the proposed model.
The phenomenon of misinformation spreading in social media has developed a new form of active citizens who focus on tackling the problem by refuting posts that might contain misinformation. Automatically identifying and characterizing the behavior of such active citizens in social media is an important task in computational social science for complementing studies in misinformation analysis. In this paper, we study this task across different social media platforms (i.e., Twitter and Weibo) and languages (i.e., English and Chinese) for the first time. To this end, (1) we develop and make publicly available a new dataset of Weibo users mapped into one of the two categories (i.e., misinformation posters or active citizens); (2) we evaluate a battery of supervised models on our new Weibo dataset and an existing Twitter dataset which we repurpose for the task; and (3) we present an extensive analysis of the differences in language use between the two user categories.1
Parler, a “free speech” platform popular among conservatives, was taken offline in January 2021 due to the lack of moderation of harmful content. While other popular social media platforms were also used to spread conspiratorial, hateful and threatening content, Parler suffered the most consequences in the aftermath of the 2020 US presidential elections, having been singled out in the news coverage. Through a comparative study, we identify differences in content using #QAnon across three social media platforms, Parler, Twitter, and Gab, focusing on the volume, the amount of anti-social language, and the context of QAnon-related content over a month-long period. While the number of posts is the highest on Parler, this could be attributed to the differences in the use of hashtags on the platforms, which has consequences for other analyses. In our analysis, Parler exhibits the highest levels of anti-social language, while Gab has the highest proportion of #QAnon posts with hate terms. To get at qualitative differences in the posts, we perform analysis of named entities and narratives, focusing on differences in the focus of conversations and the levels of anti-social language of posts mentioning different groups of political figures.
Linguistic Inquiry and Word Count (LIWC), a popular tool for automated text analysis, relies on an expert-crafted internal dictionary of psychologically relevant words and their corresponding categories. While LIWC’s dictionary covers a significant portion of commonly used words, the continuous evolution of language and the usage of slang in settings such as social media requires fixed resources to be frequently updated in order to stay relevant. In this work we present LIWC-UD, an automatically generated extension to LIWC’s dictionary which includes terms defined in Urban Dictionary. While original LIWC contains 6,547 unique entries, LIWC-UD consists of 141K unique terms automatically categorized into LIWC categories with high confidence using BERT classifier. LIWC-UD covers many additional terms that are commonly used on social media platforms like Twitter. We release LIWC-UD publicly to the community as a supplement to the original LIWC lexicon.
Blockchain and Artificial Intelligence (AI) have emerged as new technologies to improve socio-economic development and change the delivery of various private and public services. These technologies also help to broaden the interactions between citizens, governments, and organizations. The goal of this workshop is to discuss how the symbiosis of blockchain and AI can help support the development of marginalized and underserved communities in the context of the emerging Industrial Revolution 4.0 trend. The workshop also aims to address the different applications of Blockchain and AI for the social good; explore ways to help recognize indigenous languages; and explore systemic approaches to coping with stress and mental health through the analysis of texts on social media during the COVID-19 pandemic.
The spread of deceptive or misleading information, commonly referred to as misinformation, poses a social, economic, and political threat. Such deceptive information spreads quickly and inexpensively. For example, with the hype around blockchain technologies, misinformation on “get rich quick” scams on the Web is rampant, as evidenced by sophisticated Twitter hacks of celebrities and many social media posts that bait unsuspecting users to visit phishing websites. Unfortunately, AI technologies have contributed to the growing pains of misinformation on the Web, with the advances in technologies such as generative adversarial deep learning techniques that can generate “deep fakes” for nefarious purposes. At the same time, researchers are working on a different set of AI technologies to combat misinformation, akin to “fighting fire with fire.” As there is no clear way to win the online “cat-and-mouse” game against fake news generators and spreaders of misinformation, we believe social media platforms could be fortified with blockchain and AI technologies to mitigate the extent of misinformation propagation in various communities worldwide. Tamper-proof blockchain techniques can provide irrefutable evidence of what content is authentic, guaranteeing how the information has evolved with provenance trails. Various AI models that could be used for detecting fake news can be served on a blockchain for the effective and transparent utility of the model. Such a synergistic combination of AI and blockchain is a burgeoning area of research. This paper outlines a proposal for combining blockchain and AI techniques for handling misinformation on the Web and highlights some of the early ongoing work in this space.
Blockchain application development has received much attention nowadays. As development is complex and challenging, a systematic approach is needed to improve the product, services, and process quality. Despite the introduction of techniques, there are still inadequate models for demonstrating the blockchain's internal architecture. Hence, there is a gap when developing the blockchain application, a gap in the modelling environment of a blockchain development application. This paper introduces a new insight into blockchain application development through extended Agent-Oriented Modelling (eAOM). eAOM is a methodology for complex socio-technical system development, and we believe that it can reduce the complexity of implementing the blockchain application. In this paper, the eAOM is used to model a blockchain-based “win a fortune” system, which includes smart contract development. It showcases the feasibility of adopting eAOM to model a blockchain enabling application. A usability survey among the novices has further validated the usability and benefits of eAOM in the blockchain enabling application development.
Development of fully featured Automatic Speech Recognition (ASR) systems for a complete language vocabulary generally requires large data repositories, massive computing power, and a stable digital network infrastructure. These conditions are not met in the case of many indigenous languages. Based on our research for over a decade in West Africa, we present a lightweight and downscaled approach to AI-based ASR and describe a set of associated experiments. The aim is to produce a variety of limited-vocabulary ASRs as a basis for the development of practically useful (mobile and radio) voice-based information services that fit needs, preferences and knowledge of local rural communities.
Depression is one of the most common mental disorders nowadays. Cases of depression is increasing significantly in Malaysia during Covid-19 pandemic. In the era of advancement of Internet technology, number of social media user is growing exponentially and became part of human lifestyle. Social media has provided a platform to their user to share their thought and feelings effortlessly.
Previous studies demonstrated that the possibility and capability of artificial intelligence technology on analyzing texts on social media for detecting depression tendency. However, most of the study are conducted on English textual content. Mandarin is ranked second popular spoken language in the world, thus it is worth to explore depression detection technique on Mandarin textual content.
In this study, BERT model is proposed for conducting depression detection on social media. Mandarin text data is targeted as there are less studies exploring to depression detection on Mandarin text. In addition, it is also worth to find out the capability and performance of pre-trained BERT model on this particular tasks especially on Mandarin language.
This is an initial study and there are more works yet to be done. This paper will focus on the dataset acquisition and analysis.
This workshop addresses successful approaches and challenges to assessing the ethical implications of artificial intelligence in policing. It is divided into three main streams: A) How to conduct an ethics assessment of AI in policing? B) Applying Explainable AI in a Policing Context; C) The Practicalities of Co-Design Between Police and Developers
This paper provides two distinct approaches to conduct an ethics assessment of AI in policing. Crucially, ethical scrutiny must engage all aspects of the policing context. The context for adopting AI is neither solely technical nor solely societal in importance; it is a socio-technical interaction whereby technological systems (law enforcement or safeguarding) are designed by humans (developers) for humans (police) with an impact on individuals (citizens, residents, suspects, victims) and their relationship with the police. With this socio-technical complexity forming the policing context, ethical assessments must be carried out encompassing both the technical and the societal (human).
This paper describes a topic model-based approach for analyzing a large-scale dataset of chat messages exchanged between the users of LiveMe, a major social live streaming platform, in the context of broadcasts involving sexually explicit content. The analysis reveals the characteristics of predatory behavior targeting minors and its criminal dimension in an accessible and explainable manner.
General Collective Intelligence or GCI is an emerging science that creates the possibility of exponentially increasing the general problem-solving ability of groups which various researchers have hypothesized to be measured by the group’s collective intelligence factor (c), which in turn has been equated with the group’s ability to solve any problem in general. This translates into radically increasing capacity for social impact so that ”wicked” social problems might be more reliably solvable where they have not proved to be in the past. This workshop explores both GCI and the patterns through which GCI might be leveraged to achieve this radically increased social impact, as well as how in the absence of GCI, groups suffer from systematic errors in decision-making that limit their collective intelligence. This workshop will explore the hypothetical limits to the collective intelligence that web science or any other collective activity can be conducted with in the absence of General Collective Intelligence, why GCI is required to overcome these limits, and the predicted consequences of not doing so.
With this half-day, in-person workshop, we attempt to collaboratively discover best practices as well as frequent pitfalls encountered when working with Web data. Participants will first be presented with different perspectives on the significance of data quality in this specific context and familiarized with existing, structured approaches for the critical reflection on and documentation of data collection processes, before being invited to share their own experiences with the collection, use and documentation of Web data. We hope to thereby inspire participants to further integrate data documentation practices into their research processes, and for us to learn from the participants’ experiences in order to improve upon existing documentation frameworks for Web data. More details of the workshop, including the planned activities can be found at https://frohleon.github.io/DocuWeb22/.