arXiv subjects

Tristan J. B. Cann

Publications and source records attributed to Tristan J. B. Cann.

3 recordsLinked to original sources

arXiv2026

Contrastive learning for video reuse detection

The incorporation of footage from other sources has long been a concern from a copyright perspective, as such usage can harm the interests of rights holders. Recent trends in the ``remixing'' of social media content and the development of bespoke stock libraries for specialist causes mean that methods to identify content reuse will have a range of applications. In this paper, we develop a framework based on a Siamese network fine tuned using contrastive learning to identify potential sources for key frames in a given video. This framework makes two contributions to analysis of large video datasets. Firstly, we demonstrate its utility at reducing complex video data to a set of key visuals typically less than 10\% of the size of the full set. Secondly, we show that these reduced sets allow for efficient and effective identification of component footage. We test this method over a set of videos compiled from a stock footage library and demonstrate its effectiveness for constituent video identification with average accuracy greater than 0.95.

cs.CV↗

arXiv2024

Timescale-agnostic characterisation for collective attention events

Online communications, and in particular social media, are a key component of how society interacts with and promotes content online. Collective attention on such content can vary wildly. The majority of breaking topics quickly fade into obscurity after only a handful of interactions, while the possibility exists for content to ``go viral'', seeing sustained interaction by large audiences over long periods. In this paper we investigate the mechanisms behind such events and introduce a new representation that enables direct comparison of events over diverse time and volume scales. We find four characteristic behaviours in the usage of hashtags on Twitter that are indicative of different patterns of attention to topics. We go on to develop an agent-based model for generating collective attention events to test the factors affecting emergence of these phenomena. This model can reproduce the characteristic behaviours seen in the Twitter dataset using a small set of parameters, and reveal that three of these behaviours instead represent a continuum determined by model parameters rather than discrete categories. These insights suggest that collective attention in social systems develops in line with a set of universal principles independent of effects inherent to system scale, and the techniques we introduce here present a valuable opportunity to infer the possible mechanisms of attention flow in online communications.

cs.SI↗

arXiv2023

Using Semantic Similarity and Text Embedding to Measure the Social Media Echo of Strategic Communications

Online discourse covers a wide range of topics and many actors tailor their content to impact online discussions through carefully crafted messages and targeted campaigns. Yet the scale and diversity of online media content make it difficult to evaluate the impact of a particular message. In this paper, we present a new technique that leverages semantic similarity to quantify the change in the discussion after a particular message has been published. We use a set of press releases from environmental organisations and tweets from the climate change debate to show that our novel approach reveals a heavy-tailed distribution of response in online discourse to strategic communications.

cs.SI↗