Bubbles bursting

• This study presents an open, extensible, and reproducible framework for investigating the implicit factors that affect personalisation in social media searches, which are not explicitly stated or visible to users, and measuring the extent of personalisation in the search results affected by these factors. • The results of a comprehensive set of experiments on Twitter carried out in this study suggest that the hypothesised factors of followees, cookies, and carry-over effect have limited impact on personalisation, with the exception of polarised topics, where the experiment revealed a notable one-sided bias in the search results. • The article presents a range of mitigation strategies to address the negative consequences of personalisation in social media search results, in line with existing research works. These include promoting greater transparency of algorithmic personlisation mechanisms for social media search results, empowering users to control the personalisation settings of their search results, encouraging healthy online search behaviours such as deliberately seeking diverse perspectives and regularly clearing internet cookies and social media search histories, and advocating for policymakers to develop policies to promote algorithmic awareness and protect user data privacy. Social media platforms implement personalisation algorithms to provide users with a tailored selection of posts under the assumption of a better experience. However, prior studies examining social media timelines revealed that, due to personalisation algorithms, social media users are more likely to encounter attitude-consistent content that reinforces their existing beliefs than information that contradicts them, creating filter bubbles and ultimately hampering their ability to make good decisions. To burst the bubbles, this paper proposes a framework for investigating and measuring the factors that affect personalisation in social media search mechanisms by controlling external noises that can mask the results and be misinterpreted as personalisation. Upon conducting a comprehensive set of experiments with Twitter as our study social media platform, we observed that users’ followees, cookies, and carry-over effect play a role in shaping personalised search results. While the extent of their influence is relatively limited, they can still lead to the introduction of biases, consequently affecting users’ opinions and judgements. In addition, the experiments also shed light on the fact that searches on polarised issues can lead to unwarranted one-sided inclinations in the results. Lastly, we argue in favour of alleviating the multifaceted societal and ethical consequences of algorithmic personalisation through encouraging users to develop healthy social media use habits and by urging social media platforms as well as policymakers to actively work towards fostering a more diverse and reliable online information ecosystem.