A framework for interrogating social media images to reveal an emergent archive of war

The visual image has long been central to how war is seen, contested and legitimised, remembered and forgotten. Archives are pivotal to these ends as is their ownership and access, from state and other official repositories through to the countless photographs scattered and hidden from a collective understanding of what war looks like in individual collections and dusty attics. With the advent and rapid development of social media, however, the amateur and the professional, the illicit and the sanctioned, the personal and the official, and the past and the present, all seem to inhabit the same connected and chaotic space.However, to even begin to render intelligible the complexity, scale and volume of what war looks like in social media archives is a considerable task, given the limitations of any traditional human-based method of collection and analysis. We thus propose the production of a series of ‘snapshots’, using computer-aided extraction and identification techniques to try to offer an experimental way in to conceiving a new imaginary of war. We were particularly interested in testing to see if twentieth century wars, obviously initially captured via pre-digital means, had become more ‘settled’ over time in terms of their remediated presence today through their visual representations and connections on social media, compared with wars fought in digital media ecologies (i.e. those fought and initially represented amidst the volume and pervasiveness of social media images).To this end, we developed a framework for automatically extracting and analysing war images that appear in social media, using both the features of the images themselves, and the text and metadata associated with each image. The framework utilises a workflow comprising four core stages: (1) information retrieval, (2) data pre-processing, (3) feature extraction, and (4) machine learning. Our corpus was drawn from the social media platforms Facebook and Flickr

University of Copenhagen Participation in TREC Health Misinformation Track 2020

In this paper, we describe our participation in the TREC Health Misinformation Track 2020. We submitted $11$ runs to the Total Recall Task and 13 runs to the Ad Hoc task. Our approach consists of 3 steps: (1) we create an initial run with BM25 and RM3; (2) we estimate credibility and misinformation scores for the documents in the initial run; (3) we merge the relevance, credibility and misinformation scores to re-rank documents in the initial run. To estimate credibility scores, we implement a classifier which exploits features based on the content and the popularity of a document. To compute the misinformation score, we apply a stance detection approach with a pretrained Transformer language model. Finally, we use different approaches to merge scores: weighted average, the distance among score vectors and rank fusion

Trends in Phishing Attacks: Suggestions for Future Research

Deception in computer-mediated communication is a widespread phenomenon. Cyber criminals are exploiting technological mediums to communicate with potential targets as these channels reduce both the deception cues and the risk of detection itself. A prevalent deception-based attack in computer-mediated communication is phishing. Prior phishing research has addressed the “bait” and “hook” components of phishing attacks, the human-computer interaction that takes place as users judge the veracity of phishing emails and websites, and the development of technologies that can aid users in identifying and rejecting these attacks. Despite the extant research on this topic, phishing attacks continue to be successful as tactics evolve rendering existing research less relevant, and users disregard the recommendations of automated phishing tools. This paper summarizes the core of phishing research, provides an update on trending attack methods, and proposes future research addressing computer credibility in a phishing context

Collective Classification for Social Media Credibility Estimation

We introduce a novel extension of the iterative classification algorithm to heterogeneous graphs and apply it to estimate credibility in social media. Given a heterogeneous graph of events, users, and websites derived from social media posts, and given prior knowledge of the credibility of a subset of graph nodes, the approach iteratively converges to a set of classifiers that estimate credibility of the remaining nodes. To measure the performance of this approach, we train on a set of manually labeled events extracted from a corpus of Twitter data and calculate the resulting receiver operating characteristic (ROC) curves. We show that collective classification outperforms independent classification approaches, implying that graph dependencies are crucial to estimating credibility in social media