[dimension]\u201d would be a typical Clipper input. <\/span><\/p>\nAs the authors point out, Clipper initially worked only on an individual user \u2192 framework basis. \u201cUsers foraged for information completely independently from others,\u201d they note (p. 1000). Their addition to Clipper is \u201casynchronous social aggregation,\u201d a feature that incorporates dimensions from other users to bolster the usefulness of such a tool. With social aggregation, dimensions can be auto-suggested, and users can have access to a pool of knowledge about the \u201cmental schemas\u201d of so many others as they have similar experiences online. The authors offer that more frequently-input dimensions are generally more valuable in terms of sensemaking, and the augmentation to Clipper that they propose would display and collate information on dimensions according to their popularity.<\/span><\/p>\nAfter this, the authors give contextual background to their perspectives on socially augmented online sensemaking. They review relevant \u00a0contemporary research on information seeking, social data, and social and collaborative sensemaking (p. 1001) to support their hypotheses about the usefulness of socially augmenting Clipper. Then, the article moves to a discussion of the interface design and features, which include autocomplete, dimension hints, a workspace pane that hovers over web pages, and a review table where users can see a final view of the clips the user has produced during their web searching activities.<\/span><\/p>\nThe next part of the article fully describes the multiple hypotheses that underscore the rationale of socially augmenting Clipper. The hypotheses fall into three basic categories: the first is about how the social aggregation of dimensions should lead to overlaps; the second is about the social use and virality of overlapping dimensions; the third is about the objective usefulness and timeliness of this information. The authors then describe the conditions of their experiments with the tool (p. 1004), and provide an assessment of their hypotheses based on this experiment. Overall, their hypotheses proved to be accurate while leaving some room for further research: \u201cour results indicated that the dimensions generated by users showed significant overlap, and that dimensions with more overlap across users were rated as more useful,\u201d they tell us (p. 1008), a prelude to this self-judgment: \u201cour results provide an important step towards a future of distributed sensemaking.\u201d At the end, they acknowledge a number of potential drawbacks, most of which emanate from conditions of variability and subjectivity among users.\u00a0<\/span><\/p>\n(This is a good place for me to begin my reflection\u2026)<\/span><\/p>\nReflection<\/b><\/p>\n
This article is very rote and straightforward. (As I mentioned, parts of it read like a technical whitepaper). With that in mind, it’s not the kind of piece that lends itself to strong opinion. If I have any, it’s a mildly negative feeling that is not so much based on the authors\u2019 intentions or the tool\u2019s efficacy as on the presumptions at the core of their method. The notion of a \u201cmental schema\u201d in particular is an under-investigated concept. I\u2019m not sure with what authority they make statements like \u201cusers build up mental models and rich knowledge representations that capture the structure of a domain in ways that serve their goals\u201d (p. 999). Obviously they provide citations, but they\u2019re now squarely in the field of psychology, where falsifiable knowledge is elusive and (I\u2019d argue) it is unethical to present this information as fact, at least without further commentary on this. How a \u201crich knowledge representation\u201d is different from that which simply goes by the name \u201cknowledge\u201d escapes me \u2014 honestly, I think it\u2019s a just a convenient conflation. That type of unusual language (and a lot of vaguely-explained jargon) pervades their writing. I dislike it because 1) it offers an air of scientific dignity to some of their claims about the way humans make sense of information, whereas what\u2019s really needed is further exploration of the psychological literature on which it\u2019s based and 2) it\u2019s bad writing. It sounds unnatural and confusing. <\/span><\/p>\nMoving away from a basic critique of writing style and language choice \u2014 I would have appreciated this more if the authors had gone into further detail about the types of information for which this is useful. I immediately took umbrage at the idea that social data necessarily means improved user experience when making sense of online content. The ethos of \u201csocial\u201d and \u201csharing\u201d underscores the business model of the web, which encourages people to constantly give their (highly profitable) data over to platforms that have a monopoly, and which function largely on network effects. Facebook and Google are as profitable as they are because they emphasize a social dynamic to user interaction, the feeling that the internet is always a community, and to not use these tools would mean being left out of the web experience. So I’m immediately suspicious of tools that simply reproduce this mindset rather than articulating and commenting on it (although I understand that social web use is now so naturalized that my take on may too erudite to be useful in a broad critique). Having said this, on a less penetrating level, I understand where this could be useful. For instance, I appreciate sites like Yelp and user product ratings when shopping online. It\u2019s just that not everything that users do online can be analogized with wanting to make a purchase.<\/span><\/p>\nQuestions<\/strong><\/span><\/p>\n\n- Based on the part on p. 1003 where they discuss motivational factors in \u201cnoticing and using social data:\u201d \u00a0why would users want to contribute to this project? Is it the same reason for working on websleuthing projects, Wikipedia, and free\/open source software? If not, what are the key differences between all these tools that rely on crowdsourcing knowledge?<\/span><\/li>\n
- For what types of items would this be most appropriate? The authors make frequent reference to a camera, but what about less concrete objects? Are there items that challenge hypotheses such as \u201cdimensions that are shared across more people will be more useful,\u201d and can we theorize why that might be?<\/li>\n
- What if this leads to a winnowing effect where majority rule effectively pushes people away from domains that they may have been interested in?<\/li>\n
- What is the relationship between socially augmented information foraging via the Clipper add-on and a) upvoting (\u00e0 la Reddit and Metafilter, if anyone remembers what that is!) and b) algorithmic social media timeline prioritization (\u00e0 la Twitter and Facebook)?<\/li>\n
- Hypothesis 3.2 (p. 1006) states that \u201cThe social condition will generate more prototypical and more useful dimensions earlier than the non-social condition.\u201d But what is this usefulness is partially a function of user suggestibility? As an appendage to this point, and a more general meta-comment on this paper \u2014 the authors are clearly addressing psychological matters when they discuss \u201cmental schema.\u201d What are the assumptions they are making the way \u201cmental schemas\u201d are created and used, and does this embed a priori <\/i>bias into the tool?<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"
Paper: Kittur, A., Peters, A. M., Diriye, A., & Bove, M. (2014). Standing on the Schemas of Giants: Socially Augmented Information Foraging. In Proceedings of the 17th ACM Conference on Computer Supported Cooperative Work & Social Computing (pp. 999\u20131010). New York, NY, USA: ACM. Leader: Emma Summary In this article, Aniket Kittur, Andrew M. Peter, […]<\/p>\n","protected":false},"author":103,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-101","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"jetpack_featured_media_url":"","_links":{"self":[{"href":"https:\/\/wordpress.cs.vt.edu\/cs6724f17\/wp-json\/wp\/v2\/posts\/101","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/wordpress.cs.vt.edu\/cs6724f17\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/wordpress.cs.vt.edu\/cs6724f17\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/wordpress.cs.vt.edu\/cs6724f17\/wp-json\/wp\/v2\/users\/103"}],"replies":[{"embeddable":true,"href":"https:\/\/wordpress.cs.vt.edu\/cs6724f17\/wp-json\/wp\/v2\/comments?post=101"}],"version-history":[{"count":10,"href":"https:\/\/wordpress.cs.vt.edu\/cs6724f17\/wp-json\/wp\/v2\/posts\/101\/revisions"}],"predecessor-version":[{"id":137,"href":"https:\/\/wordpress.cs.vt.edu\/cs6724f17\/wp-json\/wp\/v2\/posts\/101\/revisions\/137"}],"wp:attachment":[{"href":"https:\/\/wordpress.cs.vt.edu\/cs6724f17\/wp-json\/wp\/v2\/media?parent=101"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/wordpress.cs.vt.edu\/cs6724f17\/wp-json\/wp\/v2\/categories?post=101"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/wordpress.cs.vt.edu\/cs6724f17\/wp-json\/wp\/v2\/tags?post=101"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}