Facebook has considered profiling its users' personalities and using the information to target adverts.
A patent filed by the social network describes how personality characteristics, including emotional stability, could be determined from people's messages and status updates. The firm is currently embroiled in a privacy scandal over the use of its data by a political consultancy. Facebook says it has never used the personality test in its products. The patent, first filed in 2012, is in the names of Michael Nowak and Dean Eckles. Mr Nowak has worked for Facebook for 10 years, while Prof Eckles now teaches at the Massachusetts Institute of Technology. The patent has been updated twice, most recently in 2016.
'Status updates'
The
BBC has seen emails from Mr Eckles and other Facebook staff to
University of Cambridge psychologists in which they discuss analysis of
data to infer personality traits, and talk of using such research to
improve the product for users and advertisers. The political
consultancy Cambridge Analytica claimed that it used a similar
technique, known as psychographics, in its work, though it has denied
using data sourced from a Facebook personality quiz in the 2016 US presidential election. Facebook's
patent says potential sources of data could include "status updates,
notes, messages, posts, comments, or any other communications from which
linguistic data may be extracted". It says the personality
characteristics could then be stored in a user's profile and used "to
select news stories, advertisements, or recommendations of actions
presented to the user". Prof Eckles told the BBC that his research had
involved asking Facebook users to complete surveys that posed
personality questions, adding that it had been made clear that the
social network was behind the questionnaires.
He acknowledged that "psychographic [advertising] targeting
raises some distinctive ethical concerns" but said he was doubtful it
would ever have been effective.
University ban
Facebook has raised concerns about the work of University of Cambridge academics involved in similar research. Image caption
University of Cambridge researchers have done their own studies into profiling Facebook users
The Cambridge Psychometrics Centre has been banned
from the platform while Facebook investigates allegations that it
misused data. The centre denies the allegation that it handed
data gathered from a personality quiz for academic research to the
commercial company Cubeyou. "We have had several conversations
and meetings with Facebook researchers going back as far as 2011," said a
spokesman for the Psychometrics Centre. "However, Facebook has
chosen not to publish its research, and therefore users will be unaware
how their data may be being used." This is a separate case from
the one in which another academic, Dr Aleksandr Kogan, is accused of
breaking rules by passing data to Cambridge Analytica. During
congressional hearings earlier this month, Facebook's chief executive
Mark Zuckerberg appeared to link the two cases, saying "we do need to
know whether there was something bad going on at Cambridge University
overall". Academics at the Cambridge Psychometrics Centre
published an influential paper in 2013 about predicting personality from
Facebook likes, the result of research which had been under way for
some years.
The BBC has seen a 2011 email from Prof Eckles, then
a researcher at Facebook, to the Cambridge academics expressing
interest in their work. He describes undertaking similar research. "We have been using analysis of linguistic data to infer personality traits," he wrote. "We have a manuscript that is going through some revisions now." Two
years later in 2013, another Facebook employee wrote to the Cambridge
academics, apparently unaware of the previous contact. Srikant
Ayyar wrote that he had read their recent paper with interest and
continued: "Our group does similar work with the goal of improving our
product for people who use it and advertisers. …..we are growing to keep
pace with Facebook growth and growth in the data we collect." This was followed by a conference call, but plans for collaboration came to nothing. In
a statement, Facebook said: "Like many companies we apply for a wide
variety of patents to protect our intellectual property. The patent we
applied for in this area was never used in any Facebook products or
further developed."
DETERMINING USER PERSONALITY CHARACTERISTICS FROM SOCIAL NETWORKING SYSTEM COMMUNICATIONS AND CHARACTERISTICS
A social networking system obtains linguistic
data from a user's text communications on the social networking system.
For example, occurrences of words in various types of communications by
the user in the social networking system are determined. The linguistic
data and non-linguistic data associated with the user are used in a
trained model to predict one or more personality characteristics for the
user. The inferred personality characteristics are stored in connection
with the user's profile, and may be used for targeting, ranking,
selecting versions of products, and various other purposes.
CROSS REFERENCE TO RELATED APPLICATIONS
This
application is a continuation of U.S. application Ser. No. 14/465,787,
filed Aug. 21, 2014, which is a continuation of U.S. application Ser.
No. 13/608,943, filed Sep. 10, 2012, which is incorporated by reference
in its entirety.
BACKGROUND
Embodiments
of the present disclosure relate generally to social networking and,
more specifically, to determining users' personality characteristics
based on their communications in connection with the social networking
system.
Social networks, or social
utilities that track and enable connections between users (including
people, businesses, and other entities), have become prevalent in recent
years. In particular, a social networking system allows users to more
efficiently communicate information that is relevant to their friends or
other connections in the social network. Social networks typically
incorporate a system for maintaining connections among users in the
social network and links to content that are likely to be relevant to
the users. Social networks also collect and maintain information about
the users of the social network. This information may be static, such as
geographic location, employer, job type, age, music preferences,
interests, and a variety of other attributes, or it may be dynamic, such
as tracking a user's actions within the social network. This
information can then be used to target information delivery so that
information more likely to be of particular interest to a user can be
communicated to that user.
Typical social
networking systems select content for a user based on information
provided by the user and stored in a user profile and/or based on
actions performed by the user and logged by the social networking
system. Additional information about other users connected to the user
may be used to further refine content selection. However, a user's
personality also affects how the user interprets event or content and
provides insights into how the user will react to new content or to
context changes. Although user personality may be inferred based on user
interactions with online communication channels, conventional social
networking systems do not attempt to infer user personality
characteristics from user interactions with the social networking
systems.
SUMMARY
A social
networking system obtains linguistic data from text communications
generated by the user as well as characteristics of the user maintained
by the social networking system. Based on the linguistic data and the
characteristics, the social networking system predicts one or more
personality characteristics of the user. The inferred personality
characteristics are stored in a user profile for the user and then used
to select content for presentation to the user.
Linguistic
data is extracted from one or more of a plurality of types of
communications between the user and one or more additional users of the
social networking system. Types of communications may include status
updates, notes, messages, posts, comments, or any other communications
from which linguistic data may be extracted, and the communication may
occur within the social networking system or outside of it and then
provided to the social networking system. Because different types of
communications are presented to different groups of users, extracting
linguistic data from a variety of types of communications better enables
the linguistic data to identify personality characteristics of the
user. The linguistic data may indicate a percentage of words, or word
stems, from the communications between the users and one or more
additional users that are in various categories.
In
addition to linguistic data, one or more characteristics of the user
are retrieved from a user profile associated with the user. Examples of
characteristics include: an age, a gender, a number of additional users
connected to the user, a percentage of connections to other users
initiated by the user, a presence of a profile picture in the user
profile, a number of times the user accesses the social networking
system within a specified time interval, a number of communications from
the user having different communication types, a frequency with which
the user creates different communication types, a total number of
communications generated by the user, a percentage of communications
generated by the user having different types of communication, and a
number of unique days the user generated communications having various
types of communication. One or more models are applied to the linguistic
data and the one or more characteristics to identify personality
characteristics associated with, or likely to be associated with, the
user. Examples of personality characteristics include: extroversion,
agreeableness, conscientiousness, emotional stability, and openness.
The
identified personality characteristics are stored in the user's user
profile and are used to select content for presentation to the user. For
example, the identified personality characteristics may be used along
with other information to select news stories, advertisements, or
recommendations of actions presented to the user. By using personality
characteristics when selecting content, the social networking system
increases the likelihood that the user will favorably interact with the
selected content.
BRIEF DESCRIPTION OF DRAWINGS
FIG. 1
is a block diagram of a system environment in which a social networking
system operates, in accordance with an embodiment of the invention.
FIG. 2 is a block diagram of a social networking system, in accordance with an embodiment of the invention.
FIG. 3
shows a flow chart of one embodiment of a method for determining user
personality characteristics, in accordance with an embodiment of the
invention.
The
figures depict various embodiments of the present invention for
purposes of illustration only. One skilled in the art will readily
recognize from the following discussion that alternative embodiments of
the structures and methods illustrated herein may be employed without
departing from the principles of the invention described herein.
DETAILED DESCRIPTION
System Architecture
FIG. 1 is a high level block diagram illustrating a system environment 100 for a social networking system 140. The system environment 100 comprises one or more client devices 110, a network 120, one or more third-party websites 130 and the social networking system 140. In alternative configurations, different and/or additional components may be included in the system environment 100. The embodiments described herein can be adapted to online systems that are not social networking systems.
The client devices 110
comprise one or more computing devices capable of receiving user input
as well as transmitting and/or receiving data via the network 120. In one embodiment, a client device 110 is a conventional computer system, such as a desktop or laptop computer. In another embodiment, a client device 110
may be a device having computer functionality, such as a personal
digital assistant (PDA), mobile telephone, smart-phone or similar
device. A client device 110 is configured to communicate via the network 120. In one embodiment, a client device 110 executes an application allowing a user of the client device 110 to interact with the social networking system 140. For example, a client device 110 executes a browser application to enable interaction between the client device 110 and the social networking system 140 via the network 120. In another embodiment, a client device 110 interacts with the social networking system 140 through an application programming interface (API) that runs on the native operating system of the client device 110, such as IOS® or ANDROID™.
The client devices 110 are configured to communicate via the network 120,
which may comprise any combination of local area and/or wide area
networks, using both wired and wireless communication systems. In one
embodiment, the network 120 uses standard communications technologies and/or protocols. Thus, the network 120
may include links using technologies such as Ethernet, 802.11,
worldwide interoperability for microwave access (WiMAX), 3G, 4G, CDMA,
digital subscriber line (DSL), etc. Similarly, the networking protocols
used on the network 120 may include multiprotocol label switching
(MPLS), transmission control protocol/Internet protocol (TCP/IP), User
Datagram Protocol (UDP), hypertext transport protocol (HTTP), simple
mail transfer protocol (SMTP) and file transfer protocol (FTP). Data
exchanged over the network 120 may be represented using
technologies and/or formats including hypertext markup language (HTML)
or extensible markup language (XML). In addition, all or some of links
can be encrypted using conventional encryption technologies such as
secure sockets layer (SSL), transport layer security (TLS), and Internet
Protocol security (IPsec). The third party website 130 may be coupled to the network 120 for communicating with the social networking system 140, which is further described below in conjunction with FIG. 2.
FIG. 2 is an example block diagram of an architecture of the social networking system 140. The social networking system 140 includes a user profile store 205, a content store 210, an action logger 215, an action log 220, an edge store 230, a user personality estimator 235, and a web server 240. In other embodiments, the social networking system 140
may include additional, fewer, or different components for various
applications. Conventional components such as network interfaces,
security functions, load balancers, failover servers, management and
network operations consoles, and the like are not shown so as to not
obscure the details of the system architecture.
Each user of the social networking system 140 is associated with a user profile, which is stored in the user profile store 205.
A user profile includes declarative information about the user that was
explicitly shared by the user, and may also include profile information
inferred by the social networking system 140. In one embodiment,
a user profile includes multiple data fields, each data field
describing one or more attributes of the corresponding user of the
social networking system 140. The user profile information stored in user profile store 205 describes the users of the social networking system 140,
including biographic, demographic, and other types of descriptive
information, such as work experience, educational history, gender,
hobbies or preferences, location and the like. A user profile may also
store other information provided by the user, for example, images or
videos. In certain embodiments, images of users may be tagged with
identification information of users of the social networking system 140 displayed in an image. A user profile in the user profile store 205 may also maintain references to actions by the corresponding user performed on content items in the content store 210 and stored in the action log 220.
While user profiles in the user profile store 205 are frequently associated with individuals, allowing people to interact with each other via the social networking system 140,
user profiles may also be stored for entities such as businesses or
organizations. This allows an entity to establish a presence on the
social networking system 140 for connecting and exchanging
content with other social networking system users. The entity may post
information about itself, about its products or provide other
information to users of the social networking system using a brand page
associated with the entity's user profile. Other users of the social
networking system may connect to the brand page to receive information
posted to the brand page or to receive information from the brand page. A
user profile associated with the brand page may include information
about the entity itself, providing users with background or
informational data about the entity.
The content store 210
stores objects representing various types of content. Examples of
content represented by an object include a page post, a status update, a
photo, a video, a link, a shared content item, a gaming application
achievement, a check-in event at a local business, a brand page, or any
other type of content. Objects may be created by users of the social
networking system 140, such as status updates, photos tagged by
users to be associated with other objects in the social networking
system, events, groups or applications. In some embodiments, objects are
received from third-party applications or third-party applications
separate from the social networking system 140. Content “items” represent single pieces of content that are represented as objects in the social networking system 140. Users of the social networking system 140
are encouraged to communicate with each other by posting text and
content items of various types of media through various communication
channels, increasing the interaction of users with each other and
increasing the frequency with which users interact within the social
networking system.
Social networking
system users transmit text content to each other using a variety of
communication types. The text content is stored in the content store 210
and associated with the user sending the text content and the one or
more users receiving the text content. A communication type may be
associated with the stored text content. Examples of communication types
include status updates, notes, comments, posts and messages. A status
update is text data provided by a user that is included in the user's
user profile and as stories in news feeds presented to other users
connected to the user. Similarly, a note is longer or more detailed text
data provided by the user that is included in the user's user profile
and as stories in news feeds presented to other users connected to the
user. A post is text that a user places on a profile of an additional
user connected to the user; a post is typically visible to users
connected to the user and/or users connected to the additional user.
Comments are data posted based on previously posted content, such as
status updates, notes or actions previously taken by a user. Messages
are text sent from a user to another user; messages are visible to the
user sending the message and the user receiving the message.
Communication
types differ from each other in a variety of ways. For example,
messages have smaller, more carefully chosen audiences than status
updates. As another example, notes are typically longer than other
communication types. These differences allow messages having different
communication types to provide different information about personality
characteristics of users.
The action logger 215 receives communications about user actions on and/or off the social networking system 140, populating the action log 220
with information about user actions. Such actions may include, for
example, adding a connection to another user, sending a message to
another user, uploading an image, reading a message from another user,
viewing content associated with another user, attending an event posted
by another user, among others. In some embodiments, the action logger 215 identifies interaction between a social networking system user and a brand page within the social networking system 140,
which communicates targeting criteria associated with content on the
brand page to a content selector to customize content from the brand
page. In addition, a number of actions described in connection with
other objects are directed at particular users, so these actions are
associated with those users as well. These actions are stored in the
action log 220.
The action log 220 may be used by the social networking system 140 to track user actions on the social networking system 140, as well as external website that communicate information to the social networking system 140. Users may interact with various objects on the social networking system 140,
including commenting on posts, sharing links, and checking-in to
physical locations via a mobile device, accessing content items or other
interactions. Information describing these actions is stored in the
action log 220. Additional examples of interactions with objects on the social networking system 140 included in the action log 220
include commenting on a photo album, communications between users,
becoming a fan of a musician, adding an event to a calendar, joining a
groups, becoming a fan of a brand page, creating an event, authorizing
an application, using an application and engaging in a transaction.
Additionally, the action log 220 records a user's interactions with advertisements on the social networking system 140 as well as other applications operating on the social networking system 140. In some embodiments, data from the action log 220
is used to infer interests or preferences of the user, augmenting the
interests included in the user profile and allowing a more complete
understanding of user preferences.
The action log 220
may also store user actions taken on external websites. For example, an
e-commerce website that primarily sells sporting equipment at bargain
prices may recognize a user of a social networking system 140 through social plug-ins that enable the e-commerce website to identify the user of the social networking system 140. Because users of the social networking system 140
are uniquely identifiable, e-commerce websites, such as this sporting
equipment retailer, may use the information about these users as they
visit their websites. The action log 220 records data about these
users, including webpage viewing histories, advertisements that were
engaged, purchases made, and other patterns from shopping and buying.
In one embodiment, an edge store 230 stores the information describing connections between users and other objects on the social networking system 140
in edge objects. Some edges may be defined by users, allowing users to
specify their relationships with other users. For example, users may
generate edges with other users that parallel the users' real-life
relationships, such as friends, co-workers, partners, and so forth.
Other edges are generated when users interact with objects in the social
networking system 140, such as expressing interest in a page on
the social networking system, sharing a link with other users of the
social networking system, and commenting on posts made by other users of
the social networking system.
The edge store 230
stores edge objects that include information about the edge, such as
affinity scores for objects, interests, and other users. Affinity scores
may be computed by the social networking system 140 over time to approximate a user's affinity for an object, interest, and other users in the social networking system 140 based on the actions performed by the user. A user's affinity may be computed by the social networking system 140 over time to approximate a user's affinity for an object, interest, and other users in the social networking system 140
based on the actions performed by the user. Computation of affinity is
further described in U.S. patent application Ser. No. 12/978,265, filed
on Dec. 23, 2010, which is hereby incorporated by reference in its
entirety. Multiple interactions between a user and a specific object may
be stored in one edge object in the edge store 230, in one embodiment. In some embodiments, connections between users may be stored in the user profile store 205, or the user profile store 205 may access the edge store 230 to determine connections between users.
The user personality estimator 235
determines one or more personality characteristics of a user based on
textual content generated by the user from the content store 210 and information from the user profile store 205.
Linguistic data associated with the user is determined by analyzing
text content generated by the user. This allows the user personality
estimator 235 to account for the different audiences and
information included in messages having different communication types.
To maximize the amount of linguistic data associated with the user,
messages associated with multiple communication types are retrieved from
the content store 210 and analyzed. In one embodiment, the user personality estimator 235
determines a count of words or word stems in the retrieved messages
that are in a plurality of categories. For example, the user personality
estimator 235 determines the number of word stems, such as
pronouns, emotion words, and words related to specific categories, in
messages associated with the user that are in categories of the
Linguistic Inquiry and Word Count (LIWC) data set; however, any suitable
dictionary may be used in various embodiments. As another example, one
or more matrices are constructed based on unigram or n-gram counts
obtained from the messages associated with the user.
One
or more models are applied to the linguistic data and to the data
retrieved from the user profile for the user personality estimator 235
to determine one or more personality characteristics of the user. For
example, an elastic net combining L1 and L2 penalties is applied to the
linguistic data and the information from the user profile for the user
to determine a likelihood that the user has one or more personality
characteristics. Examples of personality characteristics include
extroversion, agreeableness, conscientiousness, emotional stability and
openness; however, other personality characteristics may be determined.
One or more of the models applied by the user personality estimator 235
may be trained based on data received from a training group of users
based on survey information including questions about personality
characteristics. One or more personality characteristics determined for
the user are stored in the user's user profile and may subsequently be
used for selection of content presented to the user. The user
personality estimator 235 is further described below in conjunction with FIG. 3.
The web server 240 links the social networking system 140 via the network 120 to the one or more client devices 110, as well as to the one or more third party websites 130. The web server 240 serves web pages, as well as other web-related content, such as Java, Flash, XML and so forth. The web server 240 may provide the functionality of receiving and routing messages between the social networking system 140 and the client device 110,
for example, instant messages, queued messages (e.g., email), text and
SMS (short message service) messages, or messages sent using any other
suitable messaging technique. A user may send a request to the web
server 240 to upload information, for example, images or videos that are stored in the content store 210. Additionally, the web server 240
may provide API functionality to send data directly to native client
device operating systems, such as IOS®, ANDROID™, WEBOS® or RIM.
Determining Social Networking System User Personality Characteristics
FIG. 3 shows a flow chart of one embodiment of a method 300
for method for determining social networking system user personality
characteristics. In one embodiment, the user personality estimator 235 identifies a user of a social networking system 140 and retrieves text communications generated by the user from a content store 210. For example, the user personality estimator 235 retrieves text content generated by the user and having one or more communication types from the content store 210. As described above in conjunction with FIG. 2,
examples of communication types include status updates, notes,
comments, posts and messages. Retrieving a plurality of communication
types associated with the user allows the user personality estimator 235
to increase the amount of linguistic data for the user and to account
for the different audiences for the different communication types. The
retrieved content may be limited to content generated within a
particular time range, such as a year or multiple months.
The user personality estimator 235 extracts 305 linguistic data from the retrieved communications. For example, the user personality estimator 235
determines a count of words or word stems in the retrieved messages
that are in a plurality of categories. For example, the user personality
estimator 235 determines the number of word stems, such as
pronouns, emotion words, and words related to specific categories, in
messages associated with the user that are in categories of the
Linguistic Inquiry and Word Count (LIWC) data set; however, any other
suitable data set or combination of data sets may be used. The number of
word stems in each of the categories may be used as the linguistic data
for the user. For example, the number of word stems in each category
and the total word stem count may be used to determine proportions of
word stems matching various categories. As another example, one or more
matrices are constructed based on features determined from unigram or
n-gram counts obtained from the retrieved text content associated with
the user.
In some embodiments, the text
content associated with the user may be pre-processed to improve
linguistic data extraction. For example, proportions of word stems in
each category or proportions of each word out of the total number of
word stems or words included in the retrieved text content are
determined. Proportions for categories may be replaced by fitted values
derived from a logistic regression where category, user and
user-category pair are provided as random effects predicting
proportions. Alternatively, the user personality estimator 235
may perform topic modeling, such as latent Dirichlet allocation, to the
linguistic data from the retrieved communications to allow inference of
personality characteristics from the linguistic data without
preprocessing.
The user personality estimator 235 retrieves 310 characteristics of the user from the corresponding user profile in the user profile store 205. Examples of characteristics retrieved 310
from the user profile include: age, gender, number of additional users
connected to the user, percentage of connections to other users
initiated by the user, presence of a profile picture in the user
profile, number of times the user accesses the social networking system 140
within a specified time interval, number of communications from the
user having different communication types, frequency with which the user
creates different communication types, total number of communications
generated by the user, percentage of communications generated by the
user having different communication types and number of unique days the
user generated text content having various communication types. However,
any suitable characteristics may be retrieved 310 from the user profile.
Based
on the extracted linguistic data and the retrieved characteristics, one
or models implemented by the user personality estimator 235 determine 315
personality characteristics of the user. In one embodiment, statistical
models are applied to the linguistic data and the retrieved
characteristics to determine 315 levels the user has of
personality characteristics selected from a set of personality
characteristics. For example, the statistical models determine 315
levels the user has of one or more of extraversion, agreeableness,
conscientiousness, emotional stability and openness as personality
characteristics. In one embodiment, a Spearman rank correlation analysis
of the linguistic data is performed, allowing correlations between
personality characteristics and word stem category proportions from the
text content generated by the user to identify likely personality
characteristics. One or more models may also augment the rank
correlation analysis of linguistic data with one or more of the
characteristics retrieved 310 from the user profile. In one
embodiment, an elastic net combining L1 and L2 penalties is applied to
the linguistic data and the retrieved characteristics.
One or more of the models used by the user personality estimator 235 may be trained based on information received from a set of training users. For example, the user personality estimator 235
sends a survey to users in the set of training users and responses
received from the training set of users provide information about user
personality characteristics. In one embodiment, the survey includes two
items associated with different personality characteristics and prompts
users in the training set to agree or disagree with the application of
two adjectives to the users by selecting from a group of responses. Each
response may be assigned a value and the values for each personality
characteristic are used to determine personality characteristics of
users in the training set. After determining personality characteristics
of users in the training set, text content generated by each user in
the training set and characteristics associated with each user in the
training set are analyzed, with the resulting communication behavior
indicating characteristics and linguistic data for users having various
personality characteristics.
Based on
correlations between personality characteristics and combinations of
linguistic features and the retrieved characteristics, the user
personality estimator 235 identifies one or more personality
characteristics the user is likely to have. For example, personality
characteristics associated with at least a threshold value by one or
more models are associated with the user. In some embodiments, one or
more models used by the user personality estimator 235 account for information about other users connected to the user when determining 315
levels of personality characteristics associated with the user. For
example, a model accounts for content from one or more other users to
which text data from the user responds when using linguistic data to
determine 315 personality characteristics of the user. As another
example, overlap in word usage between the user and other users
connected to the user is a factor in determination of the user's
personality characteristics. Any other suitable information about other
users of the social networking system may be included in one or more
models to determine 315 levels of personality characteristics for the user.
The one or more personality characteristics determined 315 by the user personality estimator are stored 320 in the user profile associated with the user. In one embodiment, the user personality estimator 235
identifies a probability distribution of personality characteristics
the user is likely to have from the linguistic features and the
retrieved characteristics, and the probability distribution of
personality characteristics is stored 320 in the user profile of the user. Storing the distribution of personality characteristics allows the social networking system 140 to account for uncertainty in determination of the user's personality characteristics by storing 320 levels of personality characteristics that the user is likely to have as well as storing 320 alternative levels of personality characteristics that the user may have.
The social networking system 140 uses the personality characteristics associated with the user to select 325
additional content for the user. For example, a user's personality
characteristics may be used along with other user information, such as
affinities, to select stories for inclusion in the user's newsfeed, to
select advertisements for presentation to the user, or to select
recommendations of actions for the user to perform with the social
networking system 140. As another example, stored personality
characteristics may be used as targeting criteria for advertisers,
allowing advertisement selection to account for particular personality
characteristics to increase the likelihood that the user accesses or
otherwise positively interacts with a selected advertisement. For
example, the product presented in an advertisement may be modified based
on one or more of the personality characteristics stored in the user
profile.
Additionally, personality characteristics associated with the user may be used to select 325 content for other users of the social networking system 140.
For example, the user's personality characteristics may be used to
determine whether content associated with the user is distributed to
other users connected to the user. In one embodiment, the user's
personality characteristics may be used to determine whether stories
describing actions by the user are included in a news feed of another
user or used to determine the location of a story describing an action
by the user in the other user's news feed. As another example, the
user's personality characteristics may be used when selecting suggested
actions for other users that involve the user; as a specific example,
the user's personality characteristics may be used to determine whether
to recommend that an additional user establish a connection with the
user in the social networking system 140.
SUMMARY
The
foregoing description of the embodiments of the invention has been
presented for the purpose of illustration; it is not intended to be
exhaustive or to limit the invention to the precise forms disclosed.
Persons skilled in the relevant art can appreciate that many
modifications and variations are possible in light of the above
disclosure.
Some portions of this
description describe the embodiments of the invention in terms of
algorithms and symbolic representations of operations on information.
These algorithmic descriptions and representations are commonly used by
those skilled in the data processing arts to convey the substance of
their work effectively to others skilled in the art. These operations,
while described functionally, computationally, or logically, are
understood to be implemented by computer programs or equivalent
electrical circuits, microcode, or the like. Furthermore, it has also
proven convenient at times, to refer to these arrangements of operations
as modules, without loss of generality. The described operations and
their associated modules may be embodied in software, firmware,
hardware, or any combinations thereof.
Any
of the steps, operations, or processes described herein may be
performed or implemented with one or more hardware or software modules,
alone or in combination with other devices. In one embodiment, a
software module is implemented with a computer program product
comprising a computer-readable medium containing computer program code,
which can be executed by a computer processor for performing any or all
of the steps, operations, or processes described.
Embodiments
of the invention may also relate to an apparatus for performing the
operations herein. This apparatus may be specially constructed for the
required purposes, and/or it may comprise a general-purpose computing
device selectively activated or reconfigured by a computer program
stored in the computer. Such a computer program may be stored in a
non-transitory, tangible computer readable storage medium, or any type
of media suitable for storing electronic instructions, which may be
coupled to a computer system bus. Furthermore, any computing systems
referred to in the specification may include a single processor or may
be architectures employing multiple processor designs for increased
computing capability.
Embodiments of the
invention may also relate to a product that is produced by a computing
process described herein. Such a product may comprise information
resulting from a computing process, where the information is stored on a
non-transitory, tangible computer readable storage medium and may
include any embodiment of a computer program product or other data
combination described herein.
Finally, the
language used in the specification has been principally selected for
readability and instructional purposes, and it may not have been
selected to delineate or circumscribe the inventive subject matter. It
is therefore intended that the scope of the invention be limited not by
this detailed description, but rather by any claims that issue on an
application based hereon. Accordingly, the disclosure of the embodiments
of the invention is intended to be illustrative, but not limiting, of
the scope of the invention, which is set forth in the following claims.
Claims
1. A computer-implemented method comprising:
extracting,
by a communication network, linguistic data from at least one type of
communication between a user of the communication network and one or
more additional users of the communication network;
retrieving at least one characteristic of the user from a user profile of the user at the communication network;
applying at least statistical model to the extracted linguistic data and the at least one retrieved characteristics of the user;
selecting
at least one personality characteristics associated with at least a
threshold value from the at least one statistical model;
storing the at least one selected personality characteristic in the user profile of the user; and
presenting content to the user based at least in part on the at least one selected personality characteristic.
2. The computer-implemented method of claim 1, wherein the at least statistical model is generated by:
providing
a survey to a training set of users of the communication network, the
survey including a plurality of items associated with one or more
personality characteristics;
receiving responses to the survey from responding users in the training set;
determining
one or more personality characteristics associated with each of the
responding users based on the received responses and characteristics
associated with the responding users from user profiles at the
communication network; and
generating a model
determining one or more personality characteristics of a user based on
linguistic data associated with the user and characteristics associated
with the user.
3. The computer-implemented method of claim 2,
wherein presenting the content to the user based at least in part on the
determined one or more personality characteristics of the user
comprises:
selecting one or more stories for inclusion in a
news feed presented to the user based at least in part on the determined
one or more personality characteristics of the user; and
presenting the selected one or more stories to the user in the news feed.
4. The computer-implemented method of claim 2,
wherein presenting content to the user based at least in part on the
determined one or more personality characteristics comprises:
selecting
one or more advertisements for presentation to the user based at least
in part on the determined one or more personality characteristics; and
presenting the selected one or more advertisements to the user.
5. The computer-implemented method of claim 4,
wherein selecting one or more advertisements for presentation to the
user based at least in part on the determined one or more personality
characteristics comprises:
selecting advertisements
associated with one or more targeting criteria matching at least one of
the determined personality characteristics.
6. The computer-implemented method of claim 1,
wherein one or more of the statistical models perform at least a rank
correlation analysis of the extracted linguistic data correlating
personality characteristics and word stem category proportions from the
linguistic data.
7. The computer-implemented method of claim 1,
wherein the at least one type of communication from which the linguistic
data is extracted includes at least one of status updates, notes,
messages, posts, or comments.
8. The computer-implemented method of claim 7,
wherein the linguistic data is extracted from the at least one type of
communication comprises:
determining a count of words
associated with a plurality of categories in a data set, wherein the
count of the word stems in each of the categories is used as the
linguistic data for the user.
9. A computer-implemented method comprising:
extracting
linguistic data from a plurality of types of text communications
between a user of a communication network and one or more additional
users of the communication network;
retrieving one or
more characteristics of the user from a user profile maintained by the
communication network and associated with the user;
applying at least statistical model to the extracted linguistic data and the retrieved one or more characteristics of the user;
storing
the one or more personality characteristics determined by the generated
model in the user profile associated with the user; and
presenting content to the user based at least in part on the determined one or more personality characteristics of the user.
10. The computer-implemented method of claim 9, wherein the at least statistical model is generated by:
providing
a survey to a training set of users of the communication network, the
survey including a plurality of items associated with one or more
personality characteristics;
receiving responses to the survey from responding users in the training set;
determining
one or more personality characteristics associated with each of the
responding users based on the received responses and characteristics
associated with the responding users from user profiles at the
communication network; and
generating a model
determining one or more personality characteristics of a user based on
linguistic data associated with the user and characteristics associated
with the user.
11. The computer-implemented method of claim 9,
wherein presenting content to the user based at least in part on the
determined one or more personality characteristics comprises:
selecting
one or more recommendations for actions to the user based at least in
part on the determined one or more personality characteristics; and
presenting the selected one or more recommendations for actions to the user.
12. The computer-implemented method of claim 9,
wherein one or more of the statistical models perform at least a rank
correlation analysis of the extracted linguistic data correlating
personality characteristics and word stem category proportions from the
linguistic data.
13. The computer-implemented method of claim 9,
wherein the at least one type of communication from which the linguistic
data is extracted includes at least one of status updates, notes,
messages, posts, or comments.
14. The computer-implemented method of claim 9,
wherein the linguistic data is extracted from the at least one type of
communication comprises:
determining a count of words
associated with a plurality of categories in a data set, wherein the
count of the word stems in each of the categories is used as the
linguistic data for the user.
15. The computer-implemented method of claim 9,
wherein the one or more characteristics of the user include at least one
of an age, a gender, a number of additional users connected to the
user, a percentage of connections to other users initiated by the user, a
presence of a profile picture in the user profile, a number of times
the user accesses the social networking system within a specified time
interval, a number of communications from the user having different
communication types, a frequency with which the user creates different
communication types, a total number of communications generated by the
user, a percentage of communications generated by the user having
different types of communication, or a number of unique days the user
generated communications having various types of communication.
16. A non-transitory computer-readable storage
medium including instructions that, when executed by a processor, cause
the processor to:
extract linguistic data from at least one
type of communication between a user of a communication network and one
or more additional users of the communication network;
retrieve at least one characteristic of the user from a user profile of the user at the communication network;
apply at least statistical model to the extracted linguistic data and the at least one retrieved characteristics of the user;
select
at least one personality characteristics associated with at least a
threshold value from the at least one statistical model;
store the at least one selected personality characteristic in the user profile of the user; and
present content to the user based at least in part on the at least one selected personality characteristic.
17. The non-transitory computer readable storage medium of claim 16, wherein the at least statistical model is generated by:
providing
a survey to a training set of users of the communication network, the
survey including a plurality of items associated with one or more
personality characteristics;
receiving responses to the survey from responding users in the training set;
determining
one or more personality characteristics associated with each of the
responding users based on the received responses and characteristics
associated with the responding users from user profiles at the
communication network; and
generating a model
determining one or more personality characteristics of a user based on
linguistic data associated with the user and characteristics associated
with the user.
18. The non-transitory computer readable storage
medium of claim 16, wherein one or more of the statistical models
perform at least a rank correlation analysis of the extracted linguistic
data correlating personality characteristics and word stem category
proportions from the linguistic data.
19. The non-transitory computer readable storage
medium of claim 16, wherein the at least one type of communication from
which the linguistic data is extracted includes at least one of status
updates, notes, messages, posts, or comments.
20. The non-transitory computer readable storage
medium of claim 16, wherein the linguistic data is extracted from the at
least one type of communication comprises:
determining a
count of words associated with a plurality of categories in a data set,
wherein the count of the word stems in each of the categories is used as
the linguistic data for the user.
Patent History
Publication number: 20160283485Type:
Application
Filed: Jun 3, 2016Publication Date: Sep 29, 2016Patent Grant number: 9740752Inventors:
Michael Nowak
(San Francisco, CA),
Dean Eckles
(Cambridge, MA)
Application Number: 15/173,009