Rumor Classification through a Multimodal Fusion Framework and Ensemble Learning

2023 | Information Systems Frontiers | Citations: 0

Authors: Azri, Abderrazek; Favre, Cécile; Harbi, Nouria; Darmont, Jérôme; Noûs, Camille

Abstract: The proliferation of rumors on social media has become a major concern due to it ... Expand

Abstract: The proliferation of rumors on social media has become a major concern due to its ability to create a devastating impact. Manually assessing the veracity of social media messages is a very time-consuming task that can be much helped by machine learning. Most message veracity verification methods only exploit textual contents and metadata. Very few take both textual and visual contents, and more particularly images, into account. Moreover, prior works have used many classical machine learning models to detect rumors. However, although recent studies have proven the effectiveness of ensemble machine learning approaches, such models have seldom been applied. Thus, in this paper, we propose a set of advanced image features that are inspired from the field of image quality assessment, and introduce the Multimodal fusiON framework to assess message veracIty in social neTwORks (MONITOR), which exploits all message features by exploring various machine learning models. Moreover, we demonstrate the effectiveness of ensemble learning algorithms for rumor detection by using five metalearning models. Eventually, we conduct extensive experiments on two real-world datasets. Results show that MONITOR outperforms state-of-the-art machine learning baselines and that ensemble models significantly increase MONITOR’s performance. Collapse

Topics: Twitter social network Python microblog logistics management

Methods: computational algorithm machine learning experiment meta learning ensemble learning

Early Detection of Rumor Veracity in Social Media

2019 | HICSS | Citations: 0

Authors: Dang, Anh; Moh’d, Abidalrahman; Islam, Aminul; Milios, Evangelos

Abstract: Rumor spread has become a significant issue in online social networks (OSNs). To ... Expand

Abstract: Rumor spread has become a significant issue in online social networks (OSNs). To mitigate and limit the spread of rumors and its detrimental effects, analyzing, detecting and better understanding rumor dynamics is required. One of the critical steps of studying rumor spread is to identify the level of the rumor truthfulness in its early stage. Understanding and identifying the level of rumor truthfulness helps prevent its viral spread and minimizes the damage a rumor may cause. In this research, we aim to debunk rumors by analyzing, visualizing, and classifying the level of rumor truthfulness from a large number of users that actively engage in rumor spread. First, we create a dataset of rumors that belong to one of five categories: “False”, “Mostly False”, “True”, “Mostly True”, and “Half True”. This dataset provides intrinsic characteristics of a rumor: topics, user’s sentiment, network structural and content features. Second, we analyze and visualize the characteristics of each rumor category to better understand its features. Third, using theories from social science and psychology, we build a feature set to classify those rumors and identify their truthfulness. The evaluation results on our new dataset show that the approach could effectively detect the truth of rumors as early as seven days. The proposed approach could be used as a valuable tool for existing fact-checking websites, such as Snopes.com or Politifact.com, to detect the veracity of rumors in its early stage automatically and educate OSN users to have a well-informed decision-making process. Collapse

Topics: false rumor Twitter decision making Reddit website

Methods: sentiment analysis machine learning experiment OpenNLP term frequency–inverse document frequency

Theories: psychological theory social sciences theory

What Influences the Dissemination of Online Rumor Messages: Message Features and Topic-congruence

2019 | International Conference on Information Systems | Citations: 0

Authors: Li, Boying; Chong, Alain

Abstract: Social media enhances information exchange efficiency, yet it also facilitates t ... Expand

Abstract: Social media enhances information exchange efficiency, yet it also facilitates the dissemination of rumors which can cause harmful consequences for companies and the society. Previous research mainly focuses on identifying online rumor messages and examining individual rumoring behavior; however, little is known about what makes some online rumor messages disseminate more than others. In this study, the effects of rumor topic-congruence and rumor message features, including cognitive appeals (i.e. information unambiguity) and affective appeals (i.e. emotional words and imagery words), on online rumor message dissemination are examined. To test the hypotheses, data were collected from Twitter and labelled via sentiment analysis and manual coding. This study contributes to previous literature by extending the conceptualization of information ambiguity and understanding how rumor topic-congruence moderates the effects of cognitive appeals and affective appeals on rumor message dissemination. Collapse

Topics: Twitter social media social influence false rumor website

Methods: qualitative coding sentiment analysis self reported survey statistical hypothesis test

Institutional vs. Non-institutional use of Social Media during Emergency Response: A Case of Twitter in 2014 Australian Bush Fire

2018 | Information Systems Frontiers | Citations: 0

Authors: Abedin, Babak; Babar, Abdul

Abstract: Social media plays a significant role in rapid propagation of information when d ... Expand

Abstract: Social media plays a significant role in rapid propagation of information when disasters occur. Among the four phases of disaster management life cycle: prevention, preparedness, response, and recovery, this paper focuses on the use of social media during the response phase. It empirically examines the use of microblogging platforms by Emergency Response Organisations (EROs) during extreme natural events, and distinguishes the use of Twitter by EROs from digital volunteers during a fire hazard occurred in Australia state of Victoria in early February 2014. We analysed 7982 tweets on this event. While traditionally theories such as World System Theory and Institutional Theory focus on the role of powerful institutional information outlets, we found that platforms like Twitter challenge such notion by sharing the power between large institutional (e.g. EROs) and smaller non-institutional players (e.g. digital volunteers) in the dissemination of disaster information. Our results highlight that both large EROs and individual digital volunteers proactively used Twitter to disseminate and distribute fire related information. We also found that the contents of tweets were more informative than directive, and that while the total number of messages posted by top EROs was higher than the non-institutional ones, non-institutions presented a greater number of retweets. Collapse

Topics: Twitter social media geographic information system natural hazard false rumor

Methods: qualitative content analysis case study social network analysis

Theories: social network theory institutional theory general systems theory

Using IT Design to Prevent Cyberbullying

2017 | Journal of Management Information Systems | Citations: 51

Authors: Lowry, Paul Benjamin; Moody, Gregory D.; Chatterjee, Sutirtha

Abstract: The rise of social media has fostered increasing instances of deviant behavior. ... Expand

Abstract: The rise of social media has fostered increasing instances of deviant behavior. Arguably, the most notable of these is cyberbullying (CB), which is an increasing global concern because of the social and financial ramifications. This has necessitated a new line of research aimed at understanding and preventing CB. Although much progress has been made in understanding CB, little is known about how to prevent it, especially through the information technology (IT) design. Based on the need for a better causal theory and more effective empirical methods to investigate and mitigate this phenomenon, we leverage the control balance theory (CBT) for system design. Our model examines the causes of CB from several novel angles, including (1) the strong nonlinear influence of control imbalances on CB, and (2) using the concept of fit to understand how different design features of information technology artifacts influence factors such as deindividuation and accountability, thus affecting control imbalance. Using an innovative factorial survey method that enabled us to manipulate IT design features to obtain a nuanced view, we tested our model with 507 adults and found strong support for our model. The results show that IT design features create a strong CB opportunity for individuals who perceive that they are controlled by others. Whether this perception is real or imagined, it creates a sense of vulnerability, prompting them to engage in CB. We can thus propose specific IT design feature manipulations that can be used to discourage CB. These results should have salient implications for researchers and social media designers, especially in developing social media networks that are safe, supportive, responsible, and constructive. Collapse

Topics: social media systems design anonymity social presence social network

Methods: design artifact survey post-hoc analysis chi squared test structural equation modeling

Theories: balance theory complexity theory