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Twitter fake accounts refer to bot accounts created by third-party organizations to influence public opinion, commercial propaganda or impersonate others. The effective identification of bot accounts is conducive to accurately judge the disseminated information for the public. However, in actual fake account identification, it is expensive and inefficient to manually label Twitter accounts, and the labeled data are usually unbalanced in classes. To this end, the authors propose a novel framework to solve these problems.

In the proposed framework, the authors introduce the concept of semi-supervised self-training learning and apply it to the real Twitter account data set from Kaggle. Specifically, the authors first train the classifier in the initial small amount of labeled account data, then use the trained classifier to automatically label large-scale unlabeled account data. Next, iteratively select high confidence instances from unlabeled data to expand the labeled data. Finally, an expanded Twitter account training set is obtained. It is worth mentioning that the resampling technique is integrated into the self-training process, and the data class is balanced at the initial stage of the self-training iteration.

The proposed framework effectively improves labeling efficiency and reduces the influence of class imbalance. It shows excellent identification results on 6 different base classifiers, especially for the initial small-scale labeled Twitter accounts.

This paper provides novel insights in identifying Twitter fake accounts. First, the authors take the lead in introducing a self-training method to automatically label Twitter accounts from the semi-supervised background. Second, the resampling technique is integrated into the self-training process to effectively reduce the influence of class imbalance on the identification effect.

Social bots are prevalent on social media. Malicious bots can severely distort the true voices of customers. This paper aims to examine social bots in the context of big data of user-generated content. In particular, the author investigates the scope of information distortion for 24 brands across seven industries. Furthermore, the author studies the mechanisms that make social bots viral. Last, approaches to detecting and preventing malicious bots are recommended.

A Twitter data set of 29 million tweets was collected. Latent Dirichlet allocation and word cloud were used to visualize unstructured big data of textual content. Sentiment analysis was used to automatically classify 29 million tweets. A fixed-effects model was run on the final panel data.

The findings demonstrate that social bots significantly distort brand-related information across all industries and among all brands under study. Moreover, Twitter social bots are significantly more effective at spreading word of mouth. In addition, social bots use volumes and emotions as major effective mechanisms to influence and manipulate the spread of information about brands. Finally, the bot detection approaches are effective at identifying bots.

As brand companies use social networks to monitor brand reputation and engage customers, it is critical for them to distinguish true consumer opinions from fake ones which are artificially created by social bots.

This is the first big data examination of social bots in the context of brand-related user-generated content.

This paper aims to examine how banks in Tanzania have been vulnerable to money laundering activities and how the banking institutions have been implicated in enabling or aiding the commission of money laundering offences, and highlights the banks’ failure or inability to prevent, detect and thwart money laundering committed through their financial systems.

The paper explores Tanzania’s anti-money laundering law and analyzes non-law factors that make the banks exposed to money laundering activities. It looks at law-related, political and economic circumstances that impinge on the banks’ efficacy to tackle money laundering offences committed through their systems. The data are sourced from policy documents, statutes, case law and literature from Tanzania and other jurisdictions.

Both law-related and non-law factors create an enabling environment for the commission of money laundering offences, and this exposes banks in Tanzania to money laundering activities. Some banks have been implicated in enabling or aiding money laundering offences. These banks have abdicated their obligations to fight against money laundering. This is attributed to the fact that the banks’ internal anti-money laundering policies, regulations and procedures are inefficient, and Tanzania’s legal framework is generally ineffective to tackle money laundering offences.

This paper uncovers a multi-faceted nature of money laundering affecting banks in Tanzania. It is recommended that Tanzania’s anti-money laundering policy should address law-related, political, economic and other factors that create an enabling environment for the commission of money laundering offences. Tanzania’s anti-money laundering law should be reformed to enhance its efficacy and, lastly, banks should reinforce their internal anti-money laundering policies and regulations and policies.

The purpose of this paper is to examine one of the largest data sets on the hashtag use of #fakenews that comprises over 14m tweets sent by more than 2.4m users.

Tweets referencing the hashtag (#fakenews) were collected for a period of over one year from January 3 to May 7 of 2018. Bot detection tools were employed, and the most retweeted posts, most mentions and most hashtags as well as the top 50 most active users in terms of the frequency of their tweets were analyzed.

The majority of the top 50 Twitter users are more likely to be automated bots, while certain users’ posts like that are sent by President Donald Trump dominate the most retweeted posts that always associate mainstream media with fake news. The most used words and hashtags show that major news organizations are frequently referenced with a focus on CNN that is often mentioned in negative ways.

The research study is limited to the examination of Twitter data, while ethnographic methods like interviews or surveys are further needed to complement these findings. Though the data reported here do not prove direct effects, the implications of the research provide a vital framework for assessing and diagnosing the networked spammers and main actors that have been pivotal in shaping discourses around fake news on social media. These discourses, which are sometimes assisted by bots, can create a potential influence on audiences and their trust in mainstream media and understanding of what fake news is.

This paper offers results on one of the first empirical research studies on the propagation of fake news discourse on social media by shedding light on the most active Twitter users who discuss and mention the term “#fakenews” in connection to other news organizations, parties and related figures.

In order to further advance the research of social bots, based on the latest research trends and in line with international research frontiers, it is necessary to understand the global research situation in social bots.

Choosing Web of Science™ Core Collections as the data sources for searching social bots research literature, this paper visually analyzes the processed items and explores the overall research progress and trends of social bots from multiple perspectives of the characteristics of publication output, major academic communities and active research topics of social bots by the method of bibliometrics.

The findings offer insights into research trends pertaining to social bots and some of the gaps are also identified. It is recommended to further expand the research objects of social bots in the future, not only focus on Twitter platform and strengthen the research of social bot real-time detection methods and the discussion of the legal and ethical issues of social bots.

Most of the existing reviews are all for the detection methods and techniques of social bots. Unlike the above reviews, this study is a systematic literature review, through the method of quantitative analysis, comprehensively sort out the research output in social bots and shows the latest research trends in this area and suggests some research indirections that need to be focused in the future. The findings will provide references for subsequent scholars to research on social bots.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-06-2021-0336.

The subject areas for this case are auditing, fraud and investigations. It is also relevant for teaching aspects of corporate governance.

This case consolidates techniques and methodologies of special investigations and demonstrates weaknesses in governance and internal controls. It is appropriate for final year undergraduate students and graduate students who have attended classes on basics of accounting and financial reporting.

The case is about institutional governance and the effects of ineptness at different levels of an organization that resulted in TAS. 133 billion being “improperly” paid out to 22 firms in the financial year 2005/2006.The case is structured to focus at the dilemma of the Director of Finance as an individual who featured in the latter stages of an extensive fraud where old unclaimable debts were revived and were being claimed and paid to fictitious assignees involving a number of Central Bank officials. However, the case seeks to interrogate issues related to financial records and controls in which the position of Director of Finance had more relevance.

Working on this case should result in enabling students to acquire expertise necessary for forensic accounting. It should also enable students to learn to gain an understanding of the practice of investigative and forensic accounting as well as an understanding of the interrelationships of the parties involved in forensic investigations.

Teaching note.