D2.1.3.1
Automatic Media Exposure Tracking Tool
Specifications and Requirements
Author(s): Ville Antila (VTT), Jussi Liikka (VTT), Ville Könönen (VTT) Confidentiality: Public
Date and status: Date: 9.8.2013 - Status: Version 1.1
Version history:
Version Date State
(draft/ /update/
final)
Author(s) OR Editor/Contributors
Remarks
1.0 28.6.2013 Final Ville Antila
1.1 9.8.2013 Final; edited based on received comments
Ville Könönen
Participants Name Organisation
VTT Ville Antila, Jussi Liikka,
Ville Könönen
VTT Technical Research Centre of Finland
Executive Summary
The purpose of this document is three-fold. At first the document provides a technical review of the available information relevant to the automatic media exposure tracking in mobile devices and PCs. Secondly, the document provides an overview of the related systems available on the market. Thirdly, the requirements based on the requirements specification workshop are presented and analysed in detail.
The key information required from the automatic media exposure tracking system includes media usage context, type of the consumed media and the related timing information. The document introduces and analyses several sources of the required information: VTT Physical Activity library and VTT Routine Library for context recognition, several Android APIs for media usage tracking and several approaches such as proxiesfor passive tracking on PCs.
The major product and services providers (Arbitron Mobile, Nielsen Mobile, Lumi Mobile) are listed in the document and the main features of the products are analyzed. Two of the providers are focusing solely on mobile devices and one has a broader view including operator data analyzation.
The requirements collection workshop for the automatic media exposure tracking tool was held at 24th of April, 2013 in Helsinki. In total 8 organizations were present in the workshop. The following information was identified as important (must have -requirements): participant background information, intermedia tracking, intramedia tracking, locationand simultaneous usage of different media.
The following information was identified as nice-to-have (should have -require- ments):participant social context, social sharingand media usage motives.
Tiivistelmä
Dokumentti lähestyy automaattiselle medianseurannan työkalulle asetettavia vaatimuksia kolmesta suunnasta. Ensimmäiseksi käydään läpi vaatimuksia tekniseltä kannalta, millaista tietoa ja miten on saatavilla mobiililaitteista (älypuhelimet, tabletit) sekä PC-koneista.
Toiseksi luodataan lyhyesti olemassa olevia ratkaisuja ja niiden ominaisuuksia. Kolmanneksi analysoidaan erityisen vaatimusmäärittelytyöpajan tuloksia.
Automaattisen medianseurannan työkalun kannalta tärkeimmät informaatiolähteet liittyvät median käytön kontekstiin, median tyyppiin ja median käytön ajoitukseen. Dokumentissa käydään läpi useita relevantteja informaatiolähteitä: VTT:n kehittämät kontekstintunnistus- kirjastot Android käyttöjärjestelmälle (VTT Physical Activity library ja VTT Routine Library), useita Android rajapintoja median käytön seuraamiseksi ja eri ratkaisuja PC:n käytön passiiviseen seurantaan.
Dokumentissa tutustutaan kolmen aiheen kannalta relevantin palveluntarjoajan (Arbitron Mobile, Nielsen Mobile, Lumi Mobile) tuotteisiin ja niiden ominaisuuksiin. Kaksi palvelun- tarjoajaa keskittyy pääsääntöisesti älypuhelimissa toimiviin ratkaisuihin ja yksi käyttää huomattavasti laajempaa menetelmäkenttää, esimerkiksi operaattoritietojen analysointia.
Automaattisen medianseurannan työkalun haluttujen ominaisuuksien tunnistamiseksi järjes- tettiin työpaja Helsingissä 24.4.2013. Työpajaan osallistui kaikkiaan edustajia kahdeksasta organisaatiosta. Seuraavat tiedot tunnistettiin järjestelmän kannalta erittäin tärkeiksi:
osallistujien taustatiedot, intermedia-käyttö, intramedia-käyttö, paikkatieto ja eri medioiden yhteiskäyttö. Lisäksi seuraavat tiedot tuottavat lisäarvoa, mutta voivat olla hankalasti automaattisesti seurattavissa (vaativat käyttäjältä toimenpiteitä):sosiaalinen konteksti, median sosiaalinen jako käyttäjien kesken, median käytön motiivit.
Table of Contents
Executive Summary ... 3
Tiivistelmä ... 4
1 Table of Tables ... 6
2 Table of Figures ... 6
3 Introduction ... 7
3.1 Purpose of the document ... 7
4 Technical background ... 8
4.1 VTT Physical Activity Library and VTT Routine Library ... 8
4.2 Research questions and overview of information sources ... 8
4.3 Computing platforms for passive tracking ... 10
4.3.1 Android platform ... 10
4.3.2 PC platform ... 11
5 Analysis of state-of-the-art ... 12
5.1 Available products and services ... 12
5.2 Relevant research methods ... 13
5.3 Related research ... 15
5.3.1 Cross-platform multi-screen user behaviour ... 15
5.3.2 Mobile media and application usage ... 17
5.3.3 Mobile information needs ... 18
6 Requirements ... 19
6.1 Workshop with industry representatives... 19
6.2 General system requirements ... 20
6.3 Requirement analysis ... 21
7 System specification ... 23
7.1 Architecture overview ... 23
7.2 Architecture overview ... 24
1.1.1 Passive mobile media tracking ... 24
1.1.2 Mobile media diary application ... 24
1.1.3 Passive PC browser tracking ... 24
1.1.4 Passive TV usage tracking (optional) ... 24
7.3 Survey design ... 24
References ... 26
1 Table of Tables
Table 1 Overview of information sources from a smartphone or tablet device. ...9
Table 2 Available data sources for passive tracking on Android platform. ... 10
Table 3 Available data sources for passive tracking on PC platform... 11
Table 4 Available products and services for tracking mobile media usage... 12
Table 5 General system requirements for automatic media exposure tracking system. ... 20
2 Table of Figures Figure 1 Context components. ...8
Figure 2 Cross-platform usage behaviours [Google, 2012]. ... 16
Figure 3 Cross-platform multi-screen user behaviour. ... 17
Figure 4. Overall architecture. ... 23
Figure 5 Survey design. ... 25
3 Introduction
3.1 Purpose of the document
The purpose of this document is to describe the technical background, relevant state-of-the-art and requirement analysis as well as an overview of the system specification for the automatic media exposure tracking system.
Section 4 describes different data sources that are available from different platforms, and how to utilize the collected data. More specifically we show the overview of VTT Physical Activity Library [1] and VTT Routine Library [2]
which we can utilize to get additional information about the user’s physical activity and user’s daily routines. We also describe the technical background of utilizing different computing platforms for data gathering. We also briefly list some useful data analytics methods.
In Section 5 we present the results of a study of state-of-the-art in mobile media tracking. We analyze available products, existing research as well as useful research methods applicable to the domain of automatic media exposure tracking.
In Section 6 includes the documentation and analysis of requirements. We present the outcome of the workshop in which the requirements were gathered as well as the analysis of these requirements and their importance.
In Section 7 we describe the system specification based on the requirements analysis.
4 Technical background
4.1 VTT Physical Activity Library and VTT Routine Library
The VTT Physical Activity Library and VTT Routine Library have a versatile set of ready components ready for tracking the mobile phone user physical and telecom activity as well as day routines and it can be extended to support other context areas as well, e.g. application usage. Figure.1 depicts the basic set of the context components offered by the library.
4.2 Research questions and overview of information sources
From the collected data we try to answer research questions such as:
“When do the users consume media, what type of media, how many minutes at once and in what situation?”
“What is the user’s preferred media type in this situation, how and when should we offer different media types?”
“Based on user profiling, what is the most appropriate media type and information for this particular user?”
For answering to these research questions we will need to classify users and applications and apply relevant statistical methods. For example we can get media type from used application (if application is for example video player). By recording user identifiers and time stamps we can calculate the usage times.
Physical activity and routines can be got from the VTT libraries described in Section 4.1.
Figure 1 Context components.
Further analysis can be done based on the real collected raw data. We can for example find out the places/locations where the most media is consumed. Based on the user’s daily routines we can try to estimate the opportune moments for media advertisements per user (or user group). However, the selection of most applicable methods depends heavily on what kind of information we are trying to find out.
Table 1 Overview of information sources from a smartphone or tablet device.
Raw data gathered with the device
Analysed data Enriched with fusion from other data sources (e.g. from a survey data)
Location indicators:
- GPS
- Cell id - Wlan MAC
addresses
Location (or address with reverse geocoding)
Label for the location in question (e.g. “home”, “work”) Reported preferences in different locations/situations Used applications:
- Application - Time of day - Usage
frequencies - Duration of use
Categorization of the application:
- Social media - News - Magazines - Internet radio Categorization source:
- Android Market crawling
Reported preferences and usage frequencies for different media (e.g. which media is followed and how often) Actual usage frequencies and preferences using other media sources (e.g. TV, radio)
Browser events:
- URLs
- Time of day - Usage
frequencies - Duration of use
Categorization of the browsed URLs:
- News - Social media - Social networking - Web mail
Categorization source:
- https://www.xforce- security.com/apploupe/a pps/
Reported preferences and usage frequencies for different media (e.g. which media is followed and how often) Actual usage frequencies and preferences using other media sources (e.g. TV, radio)
Surrounding Bluetooth devices
Social surroundings:
- Known or unknown (e.g. familiar vs.
unfamiliar location) - Friends/family/co-
workers
Reported preferences in different social situations
Different sensors (accelerometer, gyroscope, light, orientation, magnetic field, proximity, humidity, temperature)
Physical surroundings and context:
- Inside/outside - On-the-
go/walking/running
Reported preferences in different situations (e.g. when commuting)
Fusion with other media tracking data (e.g. outside)
Communication activity:
- Messaging - Phone activity
Profiling the user:
- Activity of calling and messaging - Active or passive
communicator (e.g.
called vs. answered calls)
Reported communication activity or other related metric
4.3 Computing platforms for passive tracking
4.3.1 Android platform
This document assumes Android API level 10 (Android 2.3.3+) or higher. Not all devices can return all of the data described in this document, because they might not for example have all of the required sensors. Since the device capabilities vary, it is advisable to test the target device(s) and check if some of the APIs do not return valid data. Table 2 describes data available from Android platform.
Table 2 Available data sources for passive tracking on Android platform.
Data source API class
Application (name, class name, foreground application)
android.app.ActivityManager.*
Battery (level, charger connected, temperature, voltage)
android.os.BatteryManager Browser (accessed urls, bookmarks) android.provider.Browser
Calendar events android.provider.CalendarContract.Events
Contacts (personal data, phone numbers…) android.provider.ContactsContract Device settings (alarm/ring/voice volume,
language…)
android.provider.Settings.System java.util.Locale
Location (location from GPS, location from cell id)
android.location.*
Logs (lots of different events here) android.util.Log
android.provider.CallLog Network (airplane mode, network type,
cellid, mobile country code…)
android.telephony.TelephonyManager android.net.TrafficStats
Messaging android.database.Cursor
hackish way, read data from
"content://sms/inbox", "content://sms/sent"
Microphone, record audio android.media.MediaRecorder
Near Field Communication android.nfc
Sensors (accelerometer, gyroscope, light, orientation, magnetic field, proximity, humidity, temperature)
android.hardware.Sensor
Screen (off timeout, brightness) android.provider.Settings.System
Telephony android.telephony
Touch events <needs root if we want to track other than
our apps UI events, unreliable>
USB android.hardware.usb.*
Unique user identifier (Can generate for example from Bluetooth or WIFI Mac
android.bluetooth.BluetoothAdapter android.net.wifi.*
4.3.2 PC platform
For Windows, Linux and Mac OS X desktop computers there are several different options to get usage data and for example the browser history. Most of the options described in Table 3 need at least some configuration and/or installation on the desktop computer.
Table 3 Available data sources for passive tracking on PC platform.
Method Pros & Cons
Native code and/or library injections/detours Pros: Can get any data from computer.
Browser urls, running processes…
Cons: Must code native application.
Using already available utility programs.
ngrep –d wlan0 –T –q –W byline
“GET|POST HTTP”
Pros: Some tools exist.
Cons: May not provide all required data.
Run proxy (for Example Squid) on desktop computer.
Pros: We get the browser data.
Cons: We do not get anything else.
Cookie based tracking Pros: Easy to use.
Cons: Limited to browser usage history.
Browser plugin Pros: Track exactly what is needed from a browser.
Cons: Needs to write plugin for each of the browsers.
5 Analysis of state-of-the-art
5.1 Available products and services
Table 4 Available products and services for tracking mobile media usage.
Company Offering Features
Arbitron Mobile (former Zokem)
“… provides marketers, advertisers, wireless providers, and other
companies with a broad view of how consumers use mobile devices in their everyday lives”
“Through a holistic, opt-in measurement application, Arbitron Mobile opens up a real-time window into consumers’ behavior both online and offline”1
Fifty-one different metrics on user behavior and experience Full details on app and Internet click streams, usability, content consumption, social life, consumer context, lifestyles, and rich user profiles
Performance analysis of devices, networks, and applications Context-triggered, on-the-spot mobile questionnaires Real-time analytics engine converting raw data into information and insights White-labeled product, customized for your own purposes
Compatible with all main platforms on both smartphones and tablets, including
BlackBerry®, Android™, iPhone®, Symbian, and Windows Mobile®
Nielsen Mobile “Nielsen metrics for mobile devices (including
“connected” devices like iPads, Kindles and tablets) are already the market standard for market share, consumer satisfaction, device share, service quality, revenue share, advertising effectiveness, audience reach and other key indicators in the mobile marketplace.”
“Our monitoring of the rapid expansion of technology and adoption enables our clients to stay ahead of the steep curve.
Nielsen benchmarks mobile
Monitoring network signaling in 86 U.S. markets to count mobile subscribers and determine marketshare
Analyzing the cellphone bills of more than 65,000 mobile subscribers in the U.S.
Conducting extensive drive tests to measure quality of service in North America
Deploying On-Device Meters to measure smartphone activity Analyzing carrier server logs to understand feature phone usage behavior
Surveying mobile consumers via telephone, in-person and online surveys
advertising investment to other media platforms, and help clients maximize their marketing exposure. In addition, we uncover demographic and behavioral targets to predict which consumer segments and which tactics offer the highest potential for success”2
Lumi Mobile “Lumi’s products are built on our patent protected core platform, which has been over a decade in development, created specifically for researchers, media companies and brands”3
Passive Tracking (Reality Mine):
Track online usage consumed - Websites visited via the browser, search terms, embedded URLs Truly passive tracking - Once downloaded, the application sits on the phone silently. There is minimal impact on the participant’s day-to-day use of the phone
Track network usage - Messages sent and received, calls made and received, network signal strength, Wifi vs. GPRS Use of apps and other tools - Social networking, email, games, camera, diary, contacts, tools, apps added and removed Day in the life study - Track every location and activity throughout the day. Draw conclusions based on location, time of day, search criteria and behaviour
Secure online dashboard - Monitor respondent status and track active status of
respondents, ie app downloaded, data last uploaded, download data collected
5.2 Relevant research methods
In this section we present the relevant research methods with regards of the automatic media tracking. To allow rich data about the user’s media consumption during the day, we should use a mixed method approach. Relevant methods include at least:experience sampling methodfor capturing contextual information
2http://www.nielsen.com/us/en/nielsen-solutions/nielsen-measurement/nielsen-mobile-measurement.html
3http://www.lumimobile.com/our-products
about the user’s media consumption behaviour and data mining from automatically gathered data together with traditional surveys (for gathering subjective measurements).
Experience sampling method – experience sampling is a research methodology in which the participants are asked to stop at certain times and make notes of their experience in real time
o Experience sampling method allows researchers to capture both objective and subjective measurements from the situation. For example by asking opinions and attitudes towards different media exposure during the day coupled with automatic data gathering from that situation, we can learn more about the reasons behind the subjective evaluation
o References
"The experience sampling method" [3]
"Using the experience sampling method to evaluate ubicomp applications" [5]
"Experience sampling: Promises and pitfalls, strengths and weaknesses" [4]
o Tools
Affect-Sampler (Android) MobXamp (iPhone)
Data mining (from automatically gathered data) – data mining can be used to detect patterns from a data set and transform them into an understandable structure for further use
o Data mining is a term used to describe a combination of methods used to discover and visualize patterns from data. It involves methods from AI, machine learning and statistics.
o References
"Reality mining: sensing complex social systems" [6]
“Eigenbehaviors: identifying structure in routine” [7]
o Tools
RapidMiner [http://rapid-i.com/content/view/181/190/]
Weka Toolkit [http://www.cs.waikato.ac.nz/ml/weka/]
R [http://www.r-project.org/]
Surveys – surveys can be used to assess thoughts, opinions and attitudes.
In combination with automatic data gathering, surveys can help gain further knowledge and grounding for the findings from the actual behaviour data
5.3 Related research
5.3.1 Cross-platform multi-screen user behaviour
Users’ behaviour towards the usage of multiple devices in their daily lives has recently been studied in a Google research report [11]. The study identifies two major ways of interacting with multiple devices:sequentialandsimultaneous.
The research results also indicate that the prevalence of multi-device usage, whether it is sequential or simultaneous, makes it imperative that, businesses should enable the customers to save their shopping carts and to provide “signed- in” experiences or the ability to email the progress to them when changing between devices. The research results also indicate that the devices we choose to use at a particular time is very often driven by our context and situation.
According to the study, the cross-platform user behavior can be divided into two distinct categories:
1. Sequential – moving from one device to another at different times to accomplish a task
2. Simultaneous – using more than one device at the same time for either a related or unrelated activity
o Multi-tasking – simultaneously using more than one device for unrelated activities
o Complementary use – simultaneously using more than one device for related activity
Figure 2 Cross-platform usage behaviours [Google, 2012].
Multi-screen lessons to apply[11]:
1. The vast majority of media interactions are screen-based, marketing strategies should no longer be viewed as “digital” or “traditional”.
Businesses should understand all of the ways that people consume media, particularly digital, and tailor strategies to each channel
2. Consumers turn to their devices in various contexts. Marketing and websites should reflect the needs of a consumer on a specific screen, and conversion goals should be adjusted to account for the inherent differences in each device
3. During simultaneous usage, content viewed on one device can trigger specific behaviour on the other. Businesses should therefore not limit their conversion goals and calls to action to only the device where they were initially displayed
4. Most of the time when TV is watched, another screen is used as well.
These instances present the opportune time to convey your message and inspire action. A business’s TV strategy should be closely aligned and integrated with the marketing strategies for digital devices
5. Consumers shop differently across devices, so businesses should tailor the experience to each channel. It’s also important to optimize the shopping experience across all devices. For example, consumers need to find what they are looking for quickly and need a streamlined path to conversion, on smartphones
6. Smartphones are the backbone of our daily media use. They are the devices used most throughout the day and serve as the most common starting point for activities across multiple screens. Going mobile has become a business imperative
Figure 3 Cross-platform multi-screen user behaviour.
5.3.2 Mobile media and application usage
People are increasingly using mobile devices for accessing media such as news, social media, social networking and communication purposes. However, there are certain important distinctions in the usage patterns of different mobile devices, such as smartphones and tablets. Smartphones are often used in short bursts during the whole day, people feel that they are personal and important (“mission- critical”).
“Smartphones are mission-critical devices for “life,” with nearly 70% of smartphone users saying they “won’t leave home without it.”” [10]
Tablets on the other hand, are more used within home, in the evenings for media and entertainment use. They can be often used accompanied with a TV watching, for multi-tasking as well as complementary usages (querying more information about something seen on the TV).
“Tablets are a media consumption hub, with nearly 70% of tablet users reporting that their tablet is an “entertainment device” [10]
Also, the usage of smartphones is more context-driven than tablets. When users use smartphones, it has more relation to the situation or location than with the tablet.
“Location is the 4th most important factor enticing smartphone users to interact with ads after coupons, specific product searching, and favourite brands. It is far less important for tablet users who identify the sites they visited, apps they used, fun activities and daily routine as bigger factors than location when clicking on ads.” [10]
5.3.3 Mobile information needs
Mobile information needs are researched in recent studies [8, 9]. It has been discovered that people have certain motives and needs while on-the-go. The information needs are often related to the surrounding location or high-level task the user is doing [8]. The list of discovered motives and types of information needs are listed below:
Motives:
Casual - Undirected/semi-directed activities with a hedonistic rather than task-driven purpose
Lookup - “Known item” searching
Learn - Iterative information gathering that requires moderate interpretation and judgment
Investigate - Long-term research and planning that demands significant high-level thinking
Types:
Informational - Information about a topic
Geographic - Points of interest or directions between locations
Personal Information Management - Private information not publicly available
Transactional - Action-oriented rather than informational goals
By taking into account the needs of people for searching for information while on- the-go, we can better understand the potential media usage and direct marketing towards these types of situations. Also it is important to understand in which circumstances people are willing to act on to marketing or advertisement through mobile media.
6 Requirements
6.1 Workshop with industry representatives
Workshop was held in Helsinki on 24th of April (at Viestinnän Keskusliitto). The goal was to specify the required/desired functionalities for the automatic media tracking. The following organizations were present at the workshop: Sanoma News, Nelonen, KSF Media, Turun Sanomat, Alma media, Tietoykkönen, Radiomedia, and Levikintarkastus.
The requirements identified on the workshop include the following:
Important
o Background information – relevant information about the users participating in the survey (including demography and other prior information about media consumption)
o Intermedia tracking – which different media sources does the user use during the day?
o Intramedia tracking –within the media type (e.g. TV), which items (e.g. channels or brands) does the user use
o Location – location of the user, specifically: what media is used in which location and with what device
o Simultaneous usage – which devices are used together (and how often)
Nice-to-have
o Social context – with whom is the media consumed with
o Social sharing – who shares media content and to whom, when and how do people respond to the sharing
o Motives – why certain media is consumed with a certain device in a certain situation
6.2 General system requirements
In this section we specify the general system requirements based on the inputs from the Automex workshop. The Table 4 below includes the requirement description, further details about the requirement as well as the level of importance (on a scale ofmust-should-could).
Table 5 General system requirements for automatic media exposure tracking system.
ID Requirement Details Level
SR-1 The systemmustbe able to track which different media sources the user watches / follows / reads / listens / is exposed to during the day
Supported media sources should include:
Application usage on smartphone and tablet (passively)
Web browsing on smartphone, tablet and PC (passively) TV andradio (actively, i.e. asked from user)
Newspapersand magazines (actively, i.e. asked from user)
Must
SR-2 The systemmustbe able to trackwithin a certain media type (e.g. TV),what media items the user is watches / follows / reads / listens / is exposed to (e.g. MTV3 TV channel)
Supported intramedia sources should includeat least: all main media brands/items in Finland.TODO:
complete the list of media sources to track.
Must
SR-3 The systemmustbe able to track the location of the user with a useful resolution. The useful resolution in this case should be able to differentiate at least the following
locations:home,work and recreational activities
Supported locations should includeat least:
Home –media usage at home Work –media usage at work Recreational activities – media usage while on “freetime”
Traveling/commuting – media usage while commuting or traveling from one place to
Must
another SR-4 The systemmustbe able to
tracksimultaneous usage of different media sources (on different devices).
The sources of simultaneous usage should include:
Smartphone Tablet PC TV Radio
Must
SR-5 The systemmustbe able to collectbackground information about the users (demography and survey about media consumption behaviour)
Demography
”Favourite” media items (e.g.
Helsingin Sanomat, Talouselämä, MTV3, Yle 1, Yle 2 etc.) Rough sketch of daily routines (e.g. morning news, radio while commuting, evening news
Must
SR-6 The systemshouldbe able to track the social context of the media usage.
The social context includes identification of the following situations:
With family With colleagues With friends
With unknown people/in an unknown social situation
Should
SR-7 The systemcouldbe able to provide a way to query the user for input on the motive for accessing certain media items in a certain situation or by using a specific device
Possible motives include:
Ease of use Availability User experience
Could
SR-8 The systemcouldbe able to track social sharing of media items
Social sharing includes sharing media items through social media sharing platforms (e.g. StumbleUpon, Digg) or social networks (e.g. Twitter, Facebook)
Could
6.3 Requirement analysis
The system must be able to track which different media sources the user is exposed to during the day. These sources should include: application usage (smartphone and tablet), web browsing on different devices (smartphone, tablet, PC), TV watching, radio listening as well as newspaper and magazine reading.
From the technical perspective the system should be able to track passively as much as possible (i.e. without actively involving the user in the process). The sources which can be passively tracked are: application usage and web browsing.
The exposure to the rest of the media sources have to be collected by asking the user (i.e. media diary). Within a certain media category (e.g. TV), the system must be able differentiate between different media items (e.g. MTV3 TV channel). The system should do this passively for those media sources which can be tracked passively. Therefore the data gathered from application usage and web browsing should include information to enable the identification of the media item in question (e.g. URL).
The system must be able to track the location of the user with a useful resolution.
The useful resolution in this case should be able to differentiate at least the following locations: home, work and recreational activities. This information should be tracked passively, but should be asked from the user during the research study period (identification and naming of different places should be made as easy as possible).
The system must be able to track simultaneous usage of different media sources (on different devices). The system should be able to track the exact times of different media source usage, when possible. Additional information to align the media usage history could be considered (e.g. activity of devices such as TV could be used to align self-reported usage).
The system should be able to track the social context of the media usage. The system should be able to track the social context passively, but user should be asked to label the surrounding people during the research study period.
7 System specification
7.1 Architecture overview
The Figure 4 below depicts the overall architecture for the automatic media exposure tracking. The overall architecture includes 1) media tracking software for passively and actively gathering the data about the user’s media usage, 2) application servers and database for storing the gathered data, 3) data processing infrastructure for analyzing and combining the gathered data as well as 4) visualization of the data in a user-accessible way.
Figure 4. Overall architecture.
7.2 Architecture overview
1.1.1 Passive mobile media tracking Features
Application usage tracking Web browsing activity tracking Location tracking
Social surroundings tracking 1.1.2 Mobile media diary application
Features
TV exposure / watching Radio exposure / listening Newspaper exposure / reading Magazine exposure / reading 1.1.3 Passive PC browser tracking
Features
Web browsing activity tracking 1.1.4 Passive TV usage tracking (optional)
Features
TV activity (TV on / off)
7.3 Survey design
This section describes the initial design for the automatic media tracking survey process. It includes three phases, all in which different information is gathered from the user regarding the media consumption behavior.
1. Setup phase - Background information a. Demography
b. Media related background questionnaire
i. ”Favourite” media items (e.g. Helsingin Sanomat, Talouselämä, MTV3, Yle 1, Yle 2 etc.)
ii. Rough sketch of daily routines (e.g. morning news, radio while commuting, evening news)
2. Monitoring phase - Android application for media tracking
a. Passive tracking of daily routines (locations, movement patterns, surroundings)
b. Passive tracking of smartphone and tablet usage (Web browsing, applications)
c. Periodically querying about other consumed media (”did you watch the morning news from Yle 1?”)
3. Revision phase – Media diary (Web application)
a. Pre-filled with information gathered from the media tracking application
b. User can add missing information and modify pre-filled information (e.g. fix correct times)
Figure 5 Survey design.
References
[1] VTT Physical Activity Library, https://github.com/cavtt/VTTPhysicalActivityLibrary
[2] VTT Routine Library,https://github.com/cavtt/VTTRoutineLibrary
[3] R. Larson and M. Csikszentmihalyi, The Experience Sampling Method, New Directions for Methodology of Social and Behavioral Science, 15:41–56, 1983 [4] C. Scollon, C. Kim-Prieto, and E. Diener., Experience Sampling: Promises and pitfalls, strengths and weaknesses, Journal of Happiness Studies, 4(1):5–34, 2003
[5] Consolvo, S. and Walker, M., Using the experience sampling method to evaluate ubicomp applications,Pervasive Computing,IEEE, 2003.
[6] Eagle, N. and Pentland, A., Reality mining: sensing complex social systems, Personal and ubiquitous computing, 2006.
[7] Eagle, N. and Pentland, A., Eigenbehaviors: identifying structure in routine, Behavioral Ecology and Sociobiology, Springer, 2009.
[8] Church, K., and Smyth, Understanding the intent behind mobile information needs, IUI’09, 2009.
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