Abstract
Social micro-blogging systems such as Twitter are designed for rapid and informal communication from a large potential number of participants. Due to the volume of content received, human users must typically skim their timeline of received content and exercise judgement in selecting items for consumption, necessitating a selection process based on heuristics and content meta-data. This selection process is not well understood, yet is important due to its potential use in content management systems.In this research we have conducted an open online experiment in which participants are shown quantitative and qualitative meta-data describing two pieces of Twitter content. Without revealing the text of the tweet, participants are asked to make a selection. We observe the decisions made from 239 surveys and discover insights into human behaviour on decision making for content selection. We find that for qualitative meta-data consumption decisions are driven by online friendship and for quantitative meta-data the largest numerical value presented influences choice. Overall, the ‘number of retweets’ is found to be the most influential quantitative meta-data, while displaying multiple cues about an author׳s identity provides the strongest qualitative meta-data. When both quantitative and qualitative meta-data is presented, it is the qualitative meta-data (friendship information) that drives selection. The results are consistent with application of the Recognition heuristic, which postulates that when faced with constrained decision-making, humans will tend to exercise judgement based on cues representing familiarity. These findings are useful for future interface design for content filtering and recommendation systems.
Highlights
Micro-blogging has become a significant channel of communication that is widely used on a day-to-day basis around the world (Java et al, 2007), often through Twitter,1 the current most dominant micro-blogging service
In this paper we examine the role of metadata as cues for human decision making in content selection from the Twitter timeline
We investigate to what extent recognition of cues dominate when readers are deciding which content to consume in Twitter
Summary
Micro-blogging has become a significant channel of communication that is widely used on a day-to-day basis around the world (Java et al, 2007), often through Twitter, the current most dominant micro-blogging service. In this paper we examine the role of metadata as cues for human decision making in content selection from the Twitter timeline. One of the most fundamental, and to which many others are related, is the Recognition heuristic (Goldstein and Gigerenzer, 2002) which states: “If one of two objects is recognised and the other is not, infer that the recognised object has the higher value with respect to the criterion.” This is based on the assume that cues based on familiarity can drive human preference. One tweet is taken from the participants ‘timeline’, the stream of tweets they would usually see when browsing Twitter, so is a piece of content with which they have a relationship (typically they are already following the content author). The remainder of the paper is structured as follows: Section 2 gives an overview of work related to the experiment, Section 3 presents the experimental design and discusses details of the experiment operation and analysis, Section 4 presents and analyses the results of the experiment, while Section 5 summarises the conclusions of the work
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