9,756 research outputs found
Understanding search behaviour on mobile devices
Web search on hand-held devices has become enormously common and
popular. Although a number of studies have revealed how users
interact with search engine result pages (SERPs) on desktop
monitors, there are still only few studies related to user
interaction in mobile web search, and search results are shown in
a similar way whether on a mobile phone or a desktop. Therefore,
it is still difficult to know what happens between users and
SERPs while searching on small screens, and this means that the
current presentation of SERPs on mobile devices may not be the
best.
According to the findings from previous studies, including our
earlier work, we can confirm that search behaviour on
touch-enabled mobile devices is different from behaviour with
desktop screens, and so we need to consider a different SERP
presentation design for mobile devices. In this thesis, we
explore several user interactions during search with the aim of
improving search experience on smartphones.
First, one remarkable trend of mobile devices is their
enlargement of screen sizes during the last few years. This leads
us to look for differences in search behaviour on different sized
small screens, and if there are any, to suggest better
presentation of search results for each screen size. In the first
study, we investigated search performance, behaviour, and user
satisfaction on three small screens (3.6 inches for early
smartphones, 4.7 inches for recent smart-phones and 5.5 inches
for phablets). We found no significant differences with respect
to the efficiency of carrying out tasks. However, participants
exhibited different search behaviours on the small, medium, and
large sizes of small screens, respectively: a higher chance of
scrolling with the worst user satisfaction on the smallest
screen; fast information extraction with some hesitation before
selecting a link on the medium screen; and less eye movements on
top links on the largest screen. These results suggest that the
presentation of web search results for each screen size needs to
take into account differences in search behaviour.
Second, although people are familiar with turning pages
horizontally while reading books, vertical scrolling is the
standard option that people have available while searching on
mobile devices. So following a suggestion from the first study,
in the second study we explored the effect of horizontal and
vertical viewport control types (pagination versus scrolling)
with various positions of a correct answer in mobile web search.
Our findings suggest that although users are more familiar with
scrolling, participants spent less time to find the correct
answer with pagination, especially when the relevant result is
located beyond the page fold. In addition, participants using
scrolling exhibited less interest in lower-ranked results even if
the documents were relevant. The overall result indicates that it
is worthwhile providing different viewport controls for better
search experiences in mobile web search.
Third, snippets occupy the biggest space in each search result.
Results from a previous study suggested that snippet length
affects search performance on a desktop monitor. Due to the
smaller screen, the effect seems to be much larger on
smartphones. As one possible idea for a SERP presentation design
from the first study, we investigated appropriate snippet lengths
on mobile devices in the third study. We compared search
behaviour with three different snippet lengths, that is, one
line, two to three lines, and six or more lines of snippets on
mobile SERPs. We found that with long snippets, participants
needed longer search time for a particular task type, and the
longer time consumption provided no better search accuracy. Our
findings suggest that this search performance is related to
viewport movements and user attention.
We expect that our proposed approaches provide ways to understand
mobile web search behaviour, and that the findings can be applied
to a wide range of research areas such as human-computer
integration, information retrieval, and even social science for a
better presentation design of SERP on mobile devices
Incorporating Clicks, Attention and Satisfaction into a Search Engine Result Page Evaluation Model
Modern search engine result pages often provide immediate value to users and
organize information in such a way that it is easy to navigate. The core
ranking function contributes to this and so do result snippets, smart
organization of result blocks and extensive use of one-box answers or side
panels. While they are useful to the user and help search engines to stand out,
such features present two big challenges for evaluation. First, the presence of
such elements on a search engine result page (SERP) may lead to the absence of
clicks, which is, however, not related to dissatisfaction, so-called "good
abandonments." Second, the non-linear layout and visual difference of SERP
items may lead to non-trivial patterns of user attention, which is not captured
by existing evaluation metrics.
In this paper we propose a model of user behavior on a SERP that jointly
captures click behavior, user attention and satisfaction, the CAS model, and
demonstrate that it gives more accurate predictions of user actions and
self-reported satisfaction than existing models based on clicks alone. We use
the CAS model to build a novel evaluation metric that can be applied to
non-linear SERP layouts and that can account for the utility that users obtain
directly on a SERP. We demonstrate that this metric shows better agreement with
user-reported satisfaction than conventional evaluation metrics.Comment: CIKM2016, Proceedings of the 25th ACM International Conference on
Information and Knowledge Management. 201
Dancing to the Partisan Beat: A First Analysis of Political Communication on TikTok
TikTok is a video-sharing social networking service, whose popularity is
increasing rapidly. It was the world's second-most downloaded app in 2019.
Although the platform is known for having users posting videos of themselves
dancing, lip-syncing, or showcasing other talents, user-videos expressing
political views have seen a recent spurt. This study aims to perform a primary
evaluation of political communication on TikTok. We collect a set of US
partisan Republican and Democratic videos to investigate how users communicated
with each other about political issues. With the help of computer vision,
natural language processing, and statistical tools, we illustrate that
political communication on TikTok is much more interactive in comparison to
other social media platforms, with users combining multiple information
channels to spread their messages. We show that political communication takes
place in the form of communication trees since users generate branches of
responses to existing content. In terms of user demographics, we find that
users belonging to both the US parties are young and behave similarly on the
platform. However, Republican users generated more political content and their
videos received more responses; on the other hand, Democratic users engaged
significantly more in cross-partisan discussions.Comment: Accepted as a full paper at the 12th International ACM Web Science
Conference (WebSci 2020). Please cite the WebSci version; Second version
includes corrected typo
- …