39,072 research outputs found
Aggregating Content and Network Information to Curate Twitter User Lists
Twitter introduced user lists in late 2009, allowing users to be grouped
according to meaningful topics or themes. Lists have since been adopted by
media outlets as a means of organising content around news stories. Thus the
curation of these lists is important - they should contain the key information
gatekeepers and present a balanced perspective on a story. Here we address this
list curation process from a recommender systems perspective. We propose a
variety of criteria for generating user list recommendations, based on content
analysis, network analysis, and the "crowdsourcing" of existing user lists. We
demonstrate that these types of criteria are often only successful for datasets
with certain characteristics. To resolve this issue, we propose the aggregation
of these different "views" of a news story on Twitter to produce more accurate
user recommendations to support the curation process
When Sheep Shop: Measuring Herding Effects in Product Ratings with Natural Experiments
As online shopping becomes ever more prevalent, customers rely increasingly
on product rating websites for making purchase decisions. The reliability of
online ratings, however, is potentially compromised by the so-called herding
effect: when rating a product, customers may be biased to follow other
customers' previous ratings of the same product. This is problematic because it
skews long-term customer perception through haphazard early ratings. The study
of herding poses methodological challenges. In particular, observational
studies are impeded by the lack of counterfactuals: simply correlating early
with subsequent ratings is insufficient because we cannot know what the
subsequent ratings would have looked like had the first ratings been different.
The methodology introduced here exploits a setting that comes close to an
experiment, although it is purely observational---a natural experiment. Our key
methodological device consists in studying the same product on two separate
rating sites, focusing on products that received a high first rating on one
site, and a low first rating on the other. This largely controls for confounds
such as a product's inherent quality, advertising, and producer identity, and
lets us isolate the effect of the first rating on subsequent ratings. In a case
study, we focus on beers as products and jointly study two beer rating sites,
but our method applies to any pair of sites across which products can be
matched. We find clear evidence of herding in beer ratings. For instance, if a
beer receives a very high first rating, its second rating is on average half a
standard deviation higher, compared to a situation where the identical beer
receives a very low first rating. Moreover, herding effects tend to last a long
time and are noticeable even after 20 or more ratings. Our results have
important implications for the design of better rating systems.Comment: Submitted at WWW2018 - April 2018 (10 pages, 6 figures, 6 tables);
Added Acknowledgement
A Quantitative Approach to Understanding Online Antisemitism
A new wave of growing antisemitism, driven by fringe Web communities, is an
increasingly worrying presence in the socio-political realm. The ubiquitous and
global nature of the Web has provided tools used by these groups to spread
their ideology to the rest of the Internet. Although the study of antisemitism
and hate is not new, the scale and rate of change of online data has impacted
the efficacy of traditional approaches to measure and understand these
troubling trends. In this paper, we present a large-scale, quantitative study
of online antisemitism. We collect hundreds of million posts and images from
alt-right Web communities like 4chan's Politically Incorrect board (/pol/) and
Gab. Using scientifically grounded methods, we quantify the escalation and
spread of antisemitic memes and rhetoric across the Web. We find the frequency
of antisemitic content greatly increases (in some cases more than doubling)
after major political events such as the 2016 US Presidential Election and the
"Unite the Right" rally in Charlottesville. We extract semantic embeddings from
our corpus of posts and demonstrate how automated techniques can discover and
categorize the use of antisemitic terminology. We additionally examine the
prevalence and spread of the antisemitic "Happy Merchant" meme, and in
particular how these fringe communities influence its propagation to more
mainstream communities like Twitter and Reddit. Taken together, our results
provide a data-driven, quantitative framework for understanding online
antisemitism. Our methods serve as a framework to augment current qualitative
efforts by anti-hate groups, providing new insights into the growth and spread
of hate online.Comment: To appear at the 14th International AAAI Conference on Web and Social
Media (ICWSM 2020). Please cite accordingl
Assessing iSchools
Over the past decade, iSchools have emerged to educate the next generation of information professionals and scholars. Claiming to be edgy and innovative, how can and should these schools function in the spirit of assessment that now drives so much in the university? This essay, which explores how well we can assess iSchools, emerged from a doctoral seminar. Academic Culture and Practice, taught by Richard Cox and including four doctoral student participants and the Dean of School of Information Studies at the University of Pittsburgh, Ronald Larsen. The doctoral students, among other activities, were required to work on assignments to support a self-study for the University of Pittsburgh's reaccreditation by the Middle States Association. As we proceeded through the course, we found ourselves increasingly drawn to questions about how iSchools, in their nascent state, can assess themselves. Four major areasâreputation, evaluating productivity in scholarly publishing, student evaluation of teaching, and student satisfaction with their academic programsâthat emerged based on student interest as the seminar proceeded are discussed
MEDIA EFFECTS ON THE NEW YORK TIMESâ âTHE WOMENâS MARCH IN WASHINGTONâ VIDEO NEWS COVERAGE ON FACEBOOK
The reliance towards Facebook in regard to obtaining information becomes a news habit among the society. Considerable number of news coverage from media is accessible to Facebook which creates effects on the audience on account of the media exposure. The study is conducted for the purposes of analyzing news elements which are embedded in The New York Times' âThe Women's March in Wahsingtonâvideo news coverage on Facebook and discovering the effects of the coverage towards media audience. This study is constructed as a library research which utilizes textual and user-response analysis research methodology. The theory utilizes to support the study is Pan &Kosicki's Framing Analysis, and McComb& Shaw's Agenda-Setting theory is also applied in this study to support the framing analysis. The results of the study indicate that three salient elements of the coverage set public agenda to which the salient elements become prominent issues of the Women's March on Washington
An integrated ranking algorithm for efficient information computing in social networks
Social networks have ensured the expanding disproportion between the face of
WWW stored traditionally in search engine repositories and the actual ever
changing face of Web. Exponential growth of web users and the ease with which
they can upload contents on web highlights the need of content controls on
material published on the web. As definition of search is changing,
socially-enhanced interactive search methodologies are the need of the hour.
Ranking is pivotal for efficient web search as the search performance mainly
depends upon the ranking results. In this paper new integrated ranking model
based on fused rank of web object based on popularity factor earned over only
valid interlinks from multiple social forums is proposed. This model identifies
relationships between web objects in separate social networks based on the
object inheritance graph. Experimental study indicates the effectiveness of
proposed Fusion based ranking algorithm in terms of better search results.Comment: 14 pages, International Journal on Web Service Computing (IJWSC),
Vol.3, No.1, March 201
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