Editorial of the Special Issue on Following User Pathways: Key Contributions and Future Directions in Cross-Platform Social Media Research

Abstract

Social media and the resulting tidal wave of the available data have changed the ways and methods researchers analyze communities at scale. But the full potential for social scientists (and others) is not yet achieved. Despite the popularity of social media analysis in the past decade, few researchers invest in crossplatform analyses. This is a major oversight as a majority of online social media users have multiple social media accounts. Missing are the models and tools necessary to undertake analysis at scale across multiple platforms. Especially promising in support of cross-platform analysis is the mixed-method approach (e.g., qualitative and quantitative methods) in order to better understand how users and society interact online. This special issue "Following User Pathways" addresses methodological, analytical, conceptual, and technological challenges and opportunities of cross-platform analysis in social media ecosystems. Social media users traverse well-trodden paths across their preferred platforms, even as individual's breadth and depth may vary significantly. Research works concentrating on theories and applications of cross-platform social media are notably absent from the current discourse. This special issue continues the conversation of the "Following User Pathways" workshop introduced at association for computing machinery (ACM) factors in human-computer interaction in 2015 (Hall et al., 2016). In spite of the prevalence and availability of application programming interfaces (APIs) for data extraction, and an almost endless supply of papers and studies that focus on extraction and analysis of specific platforms, cross-platform analyses are lacking in the social media research domain. This is an oversight, in an age where over half of social media users have multiple social media profiles. 1 Chorley and Williams (2017) define social media as the set of applications having three characteristics: (1) The capability to support user-generated content. (2) The provision of a means for users to connect with one another. (3) Support for members to engage with one another. A deeper discussion on what exactly is "social media" can be found in McCay-Peet and Quan-Haase (2017) and Chorley and Williams (2017), as well as in Chorley and Williams (2017). This article discusses the merits and challenges of crossplatform analyses, and introduces four new studies in the cross-platform domain (

Health-Care Services as a Platform for Building Community Resilience among Minority Communities: An Israeli Pilot Study during the COVID-19 Outbreak

“…Incorporating data from local authorities and health services about availability and preparedness would be an effective way of overcoming this limitation. Finally, the sampling method, which was based on snowball sampling and social media networks, may contribute to an unbalanced sample in terms of demographic characteristics and disclosure of personal information [ 74 ].…”

Do Machines Replicate Humans? Toward a Unified Understanding of Radicalizing Content on the Open Social Web

“…The ability to analyze multimodel content is further supported by the domain's general reliance on qualitative approaches. Very few studies tie multiple platforms together (Hall, Mazarakis, Chorley, & Caton, 2018), and even fewer analyze multiple contents types and/or formats (Wendlandt, Mihalcea, Boyd, & Pennebaker, 2017).…”

Utilizing Social Media For Lead Generation

“…For ranking their recommendations, they also consider additional aspects such as diversity and novelty, which can be used in addition to the basic similarity metrics. While many studies have focused on one social media platform, [13] discuss the relevance and potential of cross-platform social media analytics, an aspect which is also central to our analysis. Typically, the focus of these recommender systems is the user of the OSN.…”

“…Due to this disparity in the database sizes, many users with a Twitter profile (having been selected based on the presence of a keyword in their Twitter Bio) did not have a corresponding available LinkedIn profile (and vice versa). The challenges of a cross-platform design are exemplified in this disparity and are further discussed in [13]. Overall, the hit rate from Twitter to LinkedIn was significantly lower than that from LinkedIn to Twitter, meaning that it was much more likely that an existing LinkedIn profile could be linked to one of the Twitter accounts.…”