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This paper aims to examine the history of data leaks and investigative journalism, the techniques and technology that enable them and their influence in Australia and abroad. It explores the ethical and professional considerations of investigative journalists, how they approach privacy and information-sharing and how this differs from intelligence practice in government and industry. The paper assesses the strengths and limitations of Collaborative Investigative Reporting based on Information Leaks (CIRIL) as a kind of public-facing intelligence practice.

This study draws on academic literature, source material from investigations by the International Consortium of Investigative Journalists and the Organised Crime and Corruption Reporting Project, and a survey of financial crime compliance professionals conducted in 2022.

The paper identifies three key causal factors that have enabled the rise of CIRIL even as traditional journalism has declined: the digital storage of information; increasing public interest in offshore finance and tax evasion; and “virtual newsrooms” enabled by internet communications. It concludes that the primary strength of CIRIL is its creation of complex global narratives to inform the public about corruption and tax evasion, while its key weakness is that the scale and breadth of the data released makes it difficult to focus on likely criminal activity. Results of a survey of industry and government professionals indicate that CIRIL is generally more effective as public information than as an investigative resource, owing to the volume, age and quality of information released. However, the trends enabling CIRIL are likely to continue, and this means that governments and financial institutions need to become more effective at using leaked information.

Over the past decade, large-scale, data-driven investigative journalism projects such as the Pandora Papers and the Russian Laundromat have had a significant public impact by exposing money laundering, financial crime and corruption. These projects share certain hallmarks: the use of human intelligence, often sourced from anonymous leaks; inventive fusion of this intelligence with data from open sources; and collaboration among a global collective of investigative journalists to build a narrative. These projects prioritise informing the public. They are also an important information source for government and private sector organisations working to investigate and disrupt financial crime.

In our studies of daily newspapers and news websites in small and medium-sized cities in Brazil, we view these enterprises as firms endowed with specific strengths and weaknesses reflecting the characteristics of the localities in which they operate. In addition, we use references from urban geography and the industrial economy to investigate their structure, conduct, and performance. This chapter presents our observations about the structure of these firms and the journalistic business in non-metropolitan cities of the State of Rio de Janeiro. The results point to greater consolidation of newspapers, despite their traditional way of operating; the low performance of news websites and their restricted source of revenue; and the existence of a potential regional market little explored by these media.

Based on a lexical analysis of publications on 529 Facebook pages, published between 2013 and 2017, this research explores how Brazilian left-wing activist groups participate on Facebook to coordinate their opposition and engage in social struggles. This chapter shows how activist groups set up two main digital network repertoires of action when mobilizing on Facebook. First, in direct connection with major political events, the platform is used as a media arena to challenge governments’ political actions and second, it is employed as a tool to coordinate mobilization, whether these mobilizations are demonstrations on the street or at cultural events, such as at a music concert. These repertoires of action exemplify ways in which contemporary Brazilian activism is carried out at the intersection of online and offline engagements. While participants engage through these two repertoires, this network of activists is held together over time through a more mundane type of event, pertaining to the repertoire of action allowing the organization of mobilization. Stepping aside from opposition and struggles brought to the streets, the organization of cultural activities, such as concerts and exhibitions, punctuates the everyday exchanges in activists’ communications. Talk about cultural events and their related social agendas structures activist networks on a medium-term basis and creates the conditions for the coordination of (future) social movements, in that they offer the opportunities to stay in contact, in addition to taking part in occasional gatherings, between more highly visible social protests.

A clickbait is a deceptive headline designed to boost ad revenue without presenting closely relevant content. There are numerous negative repercussions of clickbait, such as causing viewers to feel tricked and unhappy, causing long-term confusion, and even attracting cyber criminals. Automatic detection algorithms for clickbait have been developed to address this issue. The fact that there is only one semantic representation for the same term and a limited dataset in Chinese is a need for the existing technologies for detecting clickbait. This study aims to solve the limitations of automated clickbait detection in the Chinese dataset.

This study combines both to train the model to capture the probable relationship between clickbait news headlines and news content. In addition, part-of-speech elements are used to generate the most appropriate semantic representation for clickbait detection, improving clickbait detection performance.

This research successfully compiled a dataset containing up to 20,896 Chinese clickbait news articles. This collection contains news headlines, articles, categories and supplementary metadata. The suggested context-aware clickbait detection (CA-CD) model outperforms existing clickbait detection approaches on many criteria, demonstrating the proposed strategy's efficacy.

The originality of this study resides in the newly compiled Chinese clickbait dataset and contextual semantic representation-based clickbait detection approach employing transfer learning. This method can modify the semantic representation of each word based on context and assist the model in more precisely interpreting the original meaning of news articles.

This study examines three significant collaborative journalism projects in the Americas: The Panama Papers, from the United States-based International Consortium of Investigative Journalists (ICIJ); “América Latina, Región de Carteles,” by Colombian-based Connectas; and the first phase of the Brazilian-based project, Comprova, supported by Brazilian Association of Investigative Journalists (Abraji) and First Draft. The work investigates what encompasses collaborative journalism; and explores whether it is a recent phenomenon of the news ecosystem, a consequence of the institutional crisis of journalism, and if it is influenced by a network-based and platformed society. A mixed-method approach is applied in a three-stage analysis: (1) desk research; (2) quantitative content analysis; and (3) qualitative semi-structured in-depth interviews. To gain a broader picture of the organizations and their respective projects, documental and bibliographical research was carried out with a focus on data from press releases, corporate reports, and articles published on the websites of the organizations coordinating the projects. Furthermore, a quantitative content analysis of 10 news articles published by each of these collaboration partnerships was completed. Finally, qualitative semi-structured in-depth interviews were conducted with the directors, managers, and professional journalists’ part of the organizations and project. This study emphasizes the importance of collaborative practices, demonstrates how collaborative practices contribute to a new modus operandi of the news ecosystem; and considers why journalists and media organizations have turned to collaborative journalism as a model of production, circulation, and distribution of journalistic investigations.

The ballooning health misinformation on social media raises grave concerns. Drawing upon the S-O-R (Stimulus-Organism-Response) model and the information processing literature, this study aims to explore (1) how social media health information seeking (S) affects health misinformation sharing intention (R) through the channel of health misperceptions (O) and (2) whether the mediation process would be contingent upon different information processing predispositions.

Data were collected from a survey comprising 388 respondents from the Chinese middle-aged or above group, one of China's most susceptible populations to health misinformation. Standard multiple linear regression models and the PROCESS Macro were adopted to examine the direct effect and the moderated mediation model.

Results bolstered the S-O-R-based mechanism, in which health misperceptions mediated social media health information seeking's effect on health misinformation sharing intention. As an indicator of analytical information processing, need for cognition (NFC) failed to moderate the mediation process. Contrarily, faith in intuition (FI), an indicator reflecting intuitive information processing, served as a significant moderator. The positive association between social media health information seeking and misperceptions was stronger among respondents with low FI.

This study sheds light on health misinformation sharing research by bridging health information seeking, information internalization and information sharing. Moreover, the authors extended the S-O-R model by integrating information processing predispositions, which differs this study from previous literature and advances the extant understanding of how information processing styles work in the face of online health misinformation. The particular age group and the Chinese context further inform context-specific implications regarding online health misinformation regulation.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-04-2023-0157.

The study aims to determine the outcomes of mentorship in an online game system, as well as the characteristics of good mentors.

A combination of anonymized survey measures and in-game behavioral measures were used to power longitudinal analysis over an 11-month period in which protégés and non-mentored new players could be compared for their performance, social connections and retention.

Successful people were more likely to mentor others, and mentors increased protégés' skill. Protégés had significantly better retention, were more active and much more successful as players than non-protégés. Contrary to expectations, younger, less wealthy and educated people were more likely to be mentors and mentors did not transfer their longevity. Many of the qualities of the mentor remain largely irrelevant—what mattered most was the time spent together.

This is a study of an online game, which has unknown generalizability to other games and to offline settings.

The results show that getting mentors to spend dedicated time with protégés matters more than their characteristics.

Good mentorship does not require age or resources to provide real benefits.

This is the first study of mentorship to use survey and objective outcome measures together, over time, online.