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Mining twitter: A source for psychological wisdom
of the crowds
Ulf-Dietrich Reips &Pablo Garaizar
#Psychonomic Society, Inc. 2011
Abstract Over the last few years, microblogging has gained
prominence as a form of personal broadcasting media where
information and opinion are mixed together without an
established order, usually tightly linked with current reality.
Location awareness and promptness provide researchers using
the Internet with the opportunity to create “psychological
landscapes”—that is, to detect differences and changes in
voiced (twittered) emotions, cognitions, and behaviors. In our
article, we present iScience Maps, afreeWebservicefor
researchers, available from http://maps.iscience.deusto.es/ and
http://tweet miner.eu/. Technologically, the service is based on
Tw i t t e r ’s streaming and search application programming
interfaces (APIs), accessed through several PHP libraries, and
a JavaScript frontend. This service allows researchers to assess
via Twitter the effect of specific events in different places as
they are happening and to make comparisons between
cities, regions, or countries regarding psychological states and
their evolution in the course of an event. In a step-by-step
example, it is shown how to replicate a study on affective
and personality characteristics inferred from first names
(Mehrabian & Piercy, Personality and Social Psychology
Bulletin, 19, 755–758 1993) by mining Twitter data with
iScience Maps.Results from the original study are replicated
in both world regions we tested (the western U.S. and the
U.K./Ireland); we also discover base rate of names to be a
confound that needs to be controlled for in future research.
Keywords Twitter .Geolocation .iScience maps .
Microblogging .Internet science .Text mining .Tweet
Introduction
iScience Maps for Twitter is a set of Web applications
designed to help researchers interested in social media
analysis—specifically, mining the billions of “tweets”
(brief written messages) on Twitter that are written every
month, for scientific research. The Web service is
available from http://maps.iscience.deusto.es/ and http://
tweetminer.eu/.
Social media(a category of Web services that have
recently attracted millions of Internet users, have
become interesting resources for social-behavioral re-
search. From the traces of information created by the
behavior of the masses, the “wisdom of the crowds”
emerges. For example, David Crandall and colleagues
from Cornell University created maps of world regions
from ca. 35 million geotagged photos that had been
uploaded to flickr, a social media platform for the
exchange of pictures and attached tags (Barras, 2009).
These maps show relative interest in motifs and places and
may lead to applications in tourism, city planning,
ecology, and economics (Reips, in press). City planners
may trace such behaviorally driven location maps over
long periods and, thus, identify areas to be made
accessible via public transportation. In a similar vein,
the “wisdom of the crowds of researchers”has been
used to identify “hot topics”in psychological research.
Reips (2007,in press) reported such analyses from titles
and topics of studies on the Web experiment list and the
Web survey list, two free Web services for researchers
that help in the recruitment of participants and in the
archiving of studies (see http://wexlist.net;Reips&
Lengler, 2005).
Twitter is a Web application where users can post text-based
messages of up to 140 characters, called tweets. Apart from
this microblogging service, Twitter also works as a social
U.-D. Reips (*):P. Garaizar
University of Deusto,
Bilbao, Spain
e-mail: u.reips@ikerbasque.org
P. Garaizar
e-mail: garaizar@deusto.es
U.-D. Reips
IKERBASQUE, Basque Foundation for Science,
Bilbao, Spain
Behav Res
DOI 10.3758/s13428-011-0116-6
network, allowing its users to follow other users, group
them in lists, forward other users’messages (retweet,in
Twitter terminology), or send private messages. Figure 1
shows the Twitter Web site.
Twitter has more than 145 million registered users (Van
Grove, 2010) and produces a large amount of information
each day, 155 million tweets per day (Garrett, 2011). Thus,
it is nearly impossible to capture all this information, due to
limitations of bandwidth, storage, and rate. The percentage
of tweets that contain information about the sender’s
location is increasing but still very small (0.23% in January
2010, 0.6% in June 2010). However, rough location can
often be inferred from a user’s profile. An analysis of this
information by Semiocast (2010) shows large differences of
Twitter use between world regions and countries. More than
25% of tweets are generated in the U.S., followed by Japan
(18%), Indonesia (12%), Brazil (11%), and the U.K. (6%).
About 37% of tweeting happens in Asia, 31% in North
America, 15% in South America, 14% in Europe, and about
1.5% each in Africa and Oceania.
Simple Twitter search is available in many browsers and
online applications. Such search services are available from
Twitter directly or via the Twitter application programming
interfaces (APIs).
1
For instance, Fig. 2shows a Twitter
service that is integrated with a Web browser interface. It
automatically searches Twitter space for tweets related to
the content of the Website currently on display (in this
example, Google search results for “SCiP”) and general
trends in Twitter space. Simple Twitter search in the form of
monitoring certain terms has been used in research on
elections (Mislove, Lehmann, Ahn, Lazer, Lin, Onnela, &
Rosenquist, 2010; see http://election.ccs.neu.edu/).
Using iScience maps
We developed iScience Maps mainly to implement com-
parative searches of Twitter space. In iScience Maps, it is
possible to combine terms, using Boolean operators, and to
compare searches for different locations. The results from
some types of searches are visualized on maps—hence, the
name “iScience Maps.”The advances implemented in
iScience Maps further include the option to download
results in several formats. Generally, the tool is targeted at
behavioral researchers, while almost all other available
tools are designed for personal Twitter users, marketing
purposes, or the simple search described above.
Upon arriving at the site, the “Home”tab is displayed
(Fig. 3). Here, the visitor finds a description of the site and
brief instructions on how to use it. Using tabs, the visitor can
move to the two main types of searches, global search and
local search,andtoan“About”tab. In the present section, we
explain how to use the global and local search features
available in iScience Maps, using step-by-step examples.
Global search
Figure 4shows the screen visible to the site’s visitor in
“Global Search”—in this case, after a search for the term
1
From a technological point of view, there are three different Twitter
APIs: REST API, Search API, and Streaming API. The first two—
REST and Search API—are separated for historical reasons (Twitter
acquired Summize Inc. and rebranded it as Twitter Search) but work in
a similar way. The third one—Streaming API—was designed to
provide large amounts of data to third-party applications focused on
Twitter content analysis and works in a very different way. The Twitter
REST API methods allow developers to interact with the full range of
features regarding a specific Twitter account, using Representational
State Transfers (REST).
Fig. 1 The Twitter Website
Behav Res
“wikileaks”for a date range from October 30 to December
30 of 2010. Any dot in the resulting world map can be
clicked to summon a pop-up window with further informa-
tion about tweets and location. There is also an option for
selecting the number of intervals within the selected date
range that is used by iScience Maps to play a movie to the
user that shows the development over time. In the given
example, which the reader may execute and verify at the
Fig. 2 Twitter service that is
integrated with a Web browser
interface. It automatically
searches Twitter space for tweets
related to the content of the
Website currently on display (in
this example, Google search
results for “SCiP”) and general
trends in Twitter space
Fig. 3 iScience Maps site at
http://maps.iscience.deusto.es/
and http://tweetminer.eu/
Behav Res
iScience Maps Website, the frequency of tweets changed
dramatically when WikiLeaks suddenly made the headlines
all over the world. Figure 4shows the seventh interval out
of ten, as indicated by the large dot located above the movie
controls in the lower part of the screen.
Via “Advanced Search”and using the “+”button,
searches can be expanded to any number of terms that are
combined by Boolean search operators. For instance, in
the example above, one could add terms like “traitor”or
“hero”to “wikileaks”and combine them with Boolean
operators like “and”or “and not”to find out about
relative proportions of positive and negative responses to
WikiLeaks.
The layout in Global Search is divided into three panels:
one query panel at the top, one dynamic map at the middle,
and one results panel at the bottom. The results panel
displays the date range, searches, number of intervals
chosen for the movie, and the number of tweets found.
Results can be copied to the clipboard or downloaded in
Comma Separated Values (CSV) or Excel (XLS) format for
statistical analysis.
Local search
iScience Maps’“Local Search”provides two query panels
grouped side by side to easily compare searches between
places, date ranges, and search terms. Both panels work
the same way and can be used simultaneously. Search
terms from one panel can be copied to the other by one
click. Three parameters have to be defined to perform a
query:
&Where? Location can be defined using the map to set
the area range. The area boundaries will be defined by
the zoom used in the map widget. The text field above
the map can also be used to search for a location that is
then displayed in the map widget.
&When? A horizontal scale with two sliders allows users
to define a date range. Twitter provides data only for the
last 3–7 days.
&What? Pressing the “+”button initiates a Boolean
search for two search terms. Three operators (and,or,
and not) can be defined. Each text field should be used
Fig. 4 Example search for
“wikileaks”in iScience Maps’
Global Search tab. The panel at
the top shows configuration
options for date range, terms
(optional: more terms and
Boolean operators), number of
intervals in resulting animation,
and number of tweets found.
Any dot in the resulting world
map can be clicked to summon a
pop-up window (right side) with
further information about tweets
and location. Buttons at the
bottom control the animation
Behav Res
to define exactly one term. The number of terms is not
limited, and the search is case insensitive.
There are two APIs to be queried: The Twitter API that is
used in Local Search provides more results than its
alternative, but has a limit of 1,500 results. This API is
slow if there are too many results (15 seconds per query),
the date range is limited, and there is a 2,500 km maximum
range. The iScience API that is used in Global Search has
no result limits, no date range limits, and no distance range
limits but shows geotagged tweets only (i.e., there is no
profile-based location inference). It draws on a random
sample of 1%–10% of all tweets.
Results panel
All queries’results will be stored in a dynamic table, which
can be reordered by clicking the column headers. There are
three buttons at the top of the panel to export the results to
the clipboard or download them in CSVor Excel format.
A step-by-step example using local search
In this section, we describe how to run a partial replication of
a study on affective and personality characteristics inferred
from first names, published in 1993 by Mehrabian and
Piercy. From Table 2 in their article, we take the first six male
names; for three of these, (Alexander, Charles, Kenneth) the
connotation of the dimension “successful”was strong, and
for three (Otis, Tyrone, Wilbur), it was weak. Successful
meant “ambitious,”“intelligent,”and “creative.”If these
names’having the connotation of a personality characteristic
really holds, this likely should be apparent when Twitter is
mined, because attributions to persons, such as “Charles is an
intelligent guy,”frequently appear in text-based message
services like Twitter.
Method
To avoid reaching the maximum threshold for number of
tweets per search imposed by Twitter (1,500), we search for
one name only at a time, and only for a 3-day period. For
example, having selected the “Local search”tab, we take
the following steps:
1. Define locations in the two map areas: the western
U.S. in the left map, the U.K. and Ireland in the right
map. Any circular geographical area can be defined
precisely by clicking on “Text-based area definition”
just below a map, then entering the geographical
coordinates of a point and a radius. For example, the
western U.S. can be defined aproximately using the
coordinates 39.53393 (latitude) and 118.75542 (longi-
tude) and a radius of 1,120 km.
2. Define the date range using the slider “When?”Use the
last 3 days.
3. To find and later adjust for the base rate, we first do a
simple search for each name. Type “Otis”in the
“What?”text field and press “Get Results.”Repeat this
step for all of the names.
4. Then we search for each name in combination with an
attribute, Fig. 5shows a search for “Charles”in
combination with “intelligent,”comparing the U.S.
west coast with the U.K. and Ireland.
5. Scrolling down reveals the results for direct viewing
and download. Clicking on the first item in each row
(“Twitter”) connects to Twitter via the Web and shows
the actual tweets.
Results
Supporting the original findings for male names in the U.S.,
we did not find a single combination of the low-connotation
names with any of the terms “successful,”“ambitious,”
“intelligent,”and “creative.”All the high-connotation
names did indeed appear in the same tweets with some of
the aforementioned terms; for example, Alexander appeared
6 times with either “creative”or “successful”(out of a base
rate of 5,478 appearances overall). Kenneth was tweeted 15
times in combination with “successful”(base rate: 2,005),
and Charles 38 times with “creative,”“intelligent,”or
“successful”(base rate>16,760
2
).
These findings replicate for tweets from the U.K.: no
tweets for combinations of the four personality characteristics
with the low-connotation names, but some combinations for
two of the three high-connotation names. Charles appeared 15
times with either “creative”or “intelligent”(base rate: 1,621),
and Kenneth 5 times in combination with either “successful”
or “intelligent”(base rate: 323). Alexander appeared 1,215
times without any of the terms.
Critically—and this can be derived directly from our
Twitter study—the base rate of high-connotation versus
low-connotation names (Otis, 1,296; Tyrone, 1,324; Wilbur,
355) appears to be a confounding factor and may also
explain findings in the original study, because less frequent
names may cognitively be less associated with any
personality characteristics. Thus, to control for base rate
effects, the study would need to be complemented by
searches for combinations of names with opposite con-
notations—for example, name plus “unsuccessful.”We
2
The Twitter API limits the number of results to a maximum of 1,500
per search, so we can provide only a minimum value for this
combined result.
Behav Res
encourage the reader to use iScience Maps in doing so to
further explore and expand on the example.
Features
iScience Maps is targeted to researchers interested in
mining Twitter. It provides temporal and geospatial content
analysis and a rich set of features for comparative search
options. Trends within a date interval can be detected via
the Global Search panel and can be visualized as an
animated movie using the Scalable Vector Graphics (SVG)
based worldwide map’s animations. Local Search provides
two query forms and maps to do comparative searches. In
both clients (global and local), if the Boolean content
search field is empty, all Twitter statuses matching the
location and date range will be retrieved. Hence, it is
possible to calculate relative proportions of search term
combinations in the Twitter space for a given geo
location.
Depending on the research question, a researcher may wish
to combine the location information with aggregated data
available via zip code—for example, from the U.S. Census
We b s i t e a t http://www.census.gov/epcd/www/zipstats.html.
These data can help determine the extent to which tweeting
on a particular topic is concentrated in, for example, affluent
communities across the U.S.
Each time a researcher receives a result using a query
form, a new row is added to the table of results. This
table is a dynamic widget; thus, all its content can be
easily rearranged by just clicking on the header cells. It
also lets one export its content to the clipboard or to
CSV or Excel (XLS) format. The tweets can be accessed
as well.
Fig. 5 Comparison interface, directly available from http://maps.iscience.deusto.es/local/. The example shows a comparison for the Boolean
search “Charles”AND “intelligent”in the western U.S. and the U.K./Ireland
Behav Res
The extraordinary success of Twitter has much to do with
its APIs. APIs enable third-party services to successfully use a
platform without dealing with its implementation details,
showing it as a “black box”full of features. All of the Web
applications that are part of iScience Maps use Twitter APIs
intensively but also provide their own public API with refined
results. In this way, researchers can combine raw results from
official Twitter APIs with refined results taken from the
iScience Maps API and can cross-check trends or proportional
ratios. Another interesting feature derived from iScience
Maps’modular architecture is the possibility of linking
third-party clients that use iScience Maps’API in richer ways.
In the next section, we will compare the iScience Maps
platform and its main features with other existing services
for searching Twitter. Depending on the requirements of a
research project, some of these services may complement
iScience Maps.
Comparison of iScience maps with other Twitter search
services
There are a number of Web services that were developed
around the Twitter APIs. Not all of these services were
developed specifically for scientific research, but they may be
useful in performing certain tasks that may be needed in
research projects. For example, during the initial stage of
developing a research question, one may want to use Monitter
(http://www.monitter.com), which lets one monitor the Twitter
world in real time for a set of keywords and watch what
Twitter users are writing. In Table 1, we provide an overview
of third-party services for searches on Twitter, and we
compare their features with those provided by iScience Maps.
A second category of Web service providers do not
provide search options but provide APIs that focus on
Twitter content. Some of them offer interesting packages
like GNIP’s“Premium Twitter feeds”(http://gnip.com/
twitter): (1) Twitter Halfhose (~50% of all Twitter content,
delivered in realtime), (2) Twitter Decahose (~10% of all
Twitter content, similar to Twitter’s“Gardenhose”), (3)
Twitter Link Stream (all Twitter statuses containing URLs,
delivered in realtime), and (4) Twitter User Mention Stream
(all Twitter statuses that mention any user). The main
drawback of GNIP’s services is their steep price, which is
generally not suitable for low-budget research initiatives.
Semiocast (http://semiocast.com/) is another company
specializing in Twitter content analysis that provides
semantic analysis services through a public API. This API
can be used to analyze, filter, and prepare Twitter statuses in
terms of their language or location. Semiocast API allows
up to 1,024 API calls per day for free. 140kit (http://140kit.
com; see Gaffney, Pearce, Darham, & Nanis, 2010) is a free
Web service that enables complete data pulls for a set of
Table 1 Comparison of iScience Maps with other Twitter search applications
Service name URL Focus Near-Real-Time
Content
Arbitrary
Searches
Boolean
Search
Quantitative
Analysis
Date
Range
Geo-location Public API
Monitter http://www.monitter.com Real-time monitoring Yes Yes No No No Yes No
TwitterLocal http://www.twitterlocal.net Local business No Yes No No No Yes No
LocalTweeps http://www.localtweeps.com Twitter users No No No No No USA, Canada, UK only No
Twitspy http://twitspy.com Google Maps+ Twitter mashup Yes No No No No Yes No
MyTweetMap http://www.mytweetmap.com Twitter client +geolocation Yes No No No No Yes No
TweetMeme http://tweetmeme.com Digg +Twitter mashup Yes Yes No Yes 7 days No No
TweetStats http://tweetstats.com Per-user statistics No No No Yes No No No
Twitris http://twitris.knoesis.org Semantic Twitter analysis No No No Yes, and semantic Yes Yes No
Trendistic http://trendistic.com Trends analysis Yes Yes No Yes 180 days No No
iScience Maps http://maps.iscience.deusto.es Scientific research Yes Yes Yes Yes Yes Yes Yes
Behav Res
users or terms on Twitter, with searches running continu-
ously through Twitter streaming API running in their
servers. Those data pulls can be downloaded and processed
locally, combined with other users’data pulls, and analyzed
online, generating basic visualizations. Most of these
features can also be used through their public API.
We developed iScience Maps to work independently,
without using third-party services, but in the future we may
consider contracting some processes from third parties, if
the third-party services become more powerful and less
expensive. Like iScience Maps, GNIP provides filtered
Twitter ’s streaming API content. Although iScience Maps
compares favorably with GNIP on the cost dimension, the
gathering process of iScience Maps could be outsourced to
GNIP if they offered a “Twitter Geotagged Stream,”
providing a filtered version of Twitter Firehose with all
geotagged statuses. In a similar way, Semiocast services
could be added to iScience Maps to filter content more
deeply with its location and semantic filters. Since the
Semiocast API is a paid service (for more than 1,024
requests per day), iScience Maps’current version does not
use this service for filtering. 140kit is a useful service for
performing searches on Twitter, but only for the brief time
window of one week. 140kit only provides on-demand
Twitter data pulls; thus, a researcher would have to act
quickly and ask for a 1-week data pull on this platform.
iScience Maps’Global Search works in a more sustainable
way, since all gathered Twitter content can be queried at any
moment. Another important difference between 140kit and
iScience Maps is that the latter provides location-based
filtering, in addition to content-based filtering.
Discussion and outlook
By mining Twitter content using iScience Maps, we
replicated the findings of research inferring affective and
personality characteristics from first names (Mehrabian &
Piercy, 1993). Findings were replicated in two different
English-speaking areas of the world, the western U.S. and
the U.K./Ireland. Furthermore, we measured the base rates
of first names appearing in the same samples of tweets (in
only a matter of a few minutes, using iScience Maps). This
revealed that a crucial factor, the base rates of first names,
appears to have confounded the results in the original study.
Base rate neglect is a common cognitive phenomenon
(Kahneman & Tversky, 1972; Reips & Waldmann, 2008),
and the present results indicate that researchers are not
exempt from its effects. Thus, we tacitly conclude that our
tool has merits for conducting psychological research.
The iScience Maps Twitter tool will continue to be
developed. Currently, we are seeking contact with the
developers at Twitter. We are proposing to them a “researcher
API”that would make Twitter ’s information about tweets
much more accessible to researchers. In comparison with
Twitter ’s current APIs, the one we propose has many
benefits, including (1) very little programming work for
Twitter, (2) a reduced number of accesses to the current APIs,
and (3) helping immensely the community of researchers
who would like to use the Twitter stream in their work.
Author Note This research was first presented at the 40th Annual
Meeting of the Society for Research in Psychology (SCiP), St. Louis,
November 18, 2010. It was partially supported by grant IT363-10
from Departamento de Educación, Universidades e Investigación of
the Basque Government. We thank Unai Goikoetxeta for technical
help in setting up the servers for our local Twitter API, Ted Cascio for
copyediting, and Marc Brysbaert, Laura Buffardi, and an anonymous
reviewer for valuable feedback.
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