Zoekresultaten

Mediawijsheid: veilig en betrokken meedoen in een digitale wereld

Leerlingen groeien op in een wereld die permanent online is. Ze hebben toegang tot een grote hoeveelheid informatie en ze zijn constant online in interactie. Het onderwijs kan leerlingen opleiden tot mediawijze burgers.

The added value of advisory services in SME mergers and acquisitions

The objective of this study is to shed light on the added value of the services of five disciplines in M&A advisory in the SME domain: accountants, bankers, business brokers, fiscalists and lawyers. Theory is inconclusive in the added value of advisory services and research on the subject is hardly available. RBV predicts direct benefits in using advisory services in M&A, leading to less obstacles in and directly after M&A or lagged effects on more renewal of the firm. The theory of structural holes, agency theory and management entrenchment theory on the other hand predict neutral or negative effect of advisory services in M&A. The dataset includes 899 mergers and acquisitions (1) completed before 2003; (2) with an acquirer having bought 100% of target shares or assets; (3) of German, Belgian or Dutch origin; (4) of non-listed firms; (5) where acquirer and target firm are not member of the same family. Using (M)ANOVA’s and controlling for the effects of more than one advisor involved, the outcomes show consistently that the M&A advisory services do not reduce obstacles like financing, misinformation and culture and staff problems during or immediately after M&A. Looking at lagged effects of advisory services in the period of two years after M&A strategic more renewal by innovation occurs if bankers, fiscalists and lawyers are involved. Involvement of accountants and business brokers on the other hand decrease renewal.

The earnest platform

Increasingly, Instagram is discussed as a site for misinformation, inau-thentic activities, and polarization, particularly in recent studies aboutelections, the COVID-19 pandemic and vaccines. In this study, we havefound a different platform. By looking at the content that receives themost interactions over two time periods (in 2020) related to three U.S.presidential candidates and the issues of COVID-19, healthcare, 5G andgun control, we characterize Instagram as a site of earnest (as opposedto ambivalent) political campaigning and moral support, with a rela-tive absence of polarizing content (particularly from influencers) andlittle to no misinformation and artificial amplification practices. Mostimportantly, while misinformation and polarization might be spreadingon the platform, they do not receive much user interaction.

Explainable misinformation detection from text

Explainable Misinformation Detection from Text: A Critical Look

With the proliferation of misinformation on the web, automatic methods for detecting misinformation are becoming an increasingly important subject of study. If automatic misinformation detection is applied in a real-world setting, it is necessary to validate the methods being used. Large language models (LLMs) have produced the best results among text-based methods. However, fine-tuning such a model requires a significant amount of training data, which has led to the automatic creation of large-scale misinformation detection datasets. In this paper, we explore the biases present in one such dataset for misinformation detection in English, NELA-GT-2019. We find that models are at least partly learning the stylistic and other features of different news sources rather than the features of unreliable news. Furthermore, we use SHAP to interpret the outputs of a fine-tuned LLM and validate the explanation method using our inherently interpretable baseline. We critically analyze the suitability of SHAP for text applications by comparing the outputs of SHAP to the most important features from our logistic regression models.

Exploring Bias in Data and Models for Misinformation Detection from Text

With the proliferation of misinformation on the web, automatic misinformation detection methods are becoming an increasingly important subject of study. Large language models have produced the best results among content-based methods, which rely on the text of the article rather than the metadata or network features. However, finetuning such a model requires significant training data, which has led to the automatic creation of large-scale misinformation detection datasets. In these datasets, articles are not labelled directly. Rather, each news site is labelled for reliability by an established fact-checking organisation and every article is subsequently assigned the corresponding label based on the reliability score of the news source in question. A recent paper has explored the biases present in one such dataset, NELA-GT-2018, and shown that the models are at least partly learning the stylistic and other features of different news sources rather than the features of unreliable news. We confirm a part of their findings. Apart from studying the characteristics and potential biases of the datasets, we also find it important to examine in what way the model architecture influences the results. We therefore explore which text features or combinations of features are learned by models based on contextual word embeddings as opposed to basic bag-of-words models. To elucidate this, we perform extensive error analysis aided by the SHAP post-hoc explanation technique on a debiased portion of the dataset. We validate the explanation technique on our inherently interpretable baseline model.