This document provides an overview of how to use the Quid platform to identify emerging trends in news and blogs. It discusses how to break questions down, visualize data through different views and filters, tag content, and analyze trends over time to understand shifting topics, sentiment, and engagement. The goal is to surface insightful conversations and map relationships to inform business decisions. Strategic tagging, timeline views, and comparing coverage to social metrics help spot emerging areas and identify topics with the most consumer interest versus media focus.

1. EMERGING TRENDS
In this session you will learn how to identify and map emerging trends in the
news & blogs database using the Quid platform

2. A B OU T TH E N EW S D ATA SET
GOAL:
The goal of this session is to go through a
framework within the news database to identify
emerging trends within your areas of interest.
We will learn how to pivot the news media
landscapes into a variety of ways to better
understand trends.
Quid is a great tool for identifying emerging
trends and changes overtime - find out how to
manipulate your Quid platform to find the
answers
PURPOSE:

3. OVERVIEW OF ANALYSIS METHODOLOGY
Figure out which
visualization(s) is the
best way to represent
the data need to
answer your sub-
questions.
This may require
using the control
panel, filtering, or
tagging.
Iterate.
Once you have
determined the best
visualizations it is
important to save your
views – this way you
can come back to
them at a later date.
It also makes it easier
to modify views in the
future.
Iterate if needed
Once you have saved
all the views needed
to answer you
question – it is time to
export the data and
begin creating your
story.
The first step is to
break out your larger
business question into
smaller more explicit
questions which can
be visualized in Quid.
E.g. What is the public
narrative around IBM?
Becomes - what are
the main topics of
conversation around
IBM?
1 2 3 4
BREAKDOWN VISUALIZE SAVE VIEWS STORYBOARD

4. W H AT IS A GOOD QU ID QU ESTION ?
CHARACTERISTICS OF A GOOD QUID QUESTION:
EXAMPLE QUESTIONS:
Quid excels at strategic questions with complex, fuzzy, multi-faceted
answers
• By contrast Google excels at simple questions with fact-based,
discrete, precise answers
• A litmus test for a good Quid question is when the first answer is, “it
depends...”
Good Quid use cases will have a broad scope but with a specific lens, e.g.
“millennials and eating habits” or “top startups in IOT security”
• Analysis benefits from a summary of the overall landscape and a
drill-down into specific topics of interest
• More information yields better results because it adds valuable
context and perspective to the answer
• Insights can be derived from having a bird’s-eye-view and leveraging
the wisdom of the crowd
• Quid can help identify the right question to ask AND provide the right
insights to answer the question
Quid use cases should produce an outcome or set of outcomes that inform
a business decision
The analysis needs to be focused on something tangible
• What are my competitors doing?
• Where is my industry headed?
• What are my customers saying?
• What is public perception of my brand?
• Which trends should I care about?
• Which sectors are attracting the highest investment?

5. U N D ER STA N D IN G TH E SEA R C H D ISPLAY
Related terms:
Related terms allow you to select other search terms that
you may want to add to your query based off of your current
search terms
Articles Returned:
Once you run a query, Quid will display how many articles
were identified in our database based on the query run. Quid
also de-duplicates republished articles – so that in addition
to the total number of articles identified, the % unique
articles will also be displayed.
Relevance:
Relevance is determined by looking at instances of the
search query within the article according to 3 main
components: use of the query in the title, frequency of use in
the body, and how early within the body the query term was
used.
Filtering:
There are a variety of ways to filter the data once a query has been run
• Date Range (runs from August 2013 onwards)
• Source Country – where the publication is from
• Source Quality – designations for readership of a publication (e.g. New
York Times vs. your mom’s blog)
• Source Category – Different types of publications
Topic Cards:
Topic cards allow you to filter out articles based on pre-
determined topic tags. This allows your to remove irrelevant
articles by removing an entire topic vs. changing your search
query which can be a bit difficult and time consuming. In
addition to entire topics, you can remove specific subtopics if
only some of the articles are relevant.

6. QU ID META D ATA : N EW S & B LOGS
Metadata Description
Published Count The total number of times a given story was republished through syndication.
Earliest Published Earliest date an article was published
Latest Published Latest date an article was republished
Social Engagement
The number of times an article was engaged by consumers - including shares, reactions (likes) and comments - across several social media platforms (Facebook, LinkedIn,
Google Plus, and Pinterest).
Word Count How many words are in article
Company (Primary Mention) The most prominently mentioned company in a given story.
Companies (Any Mention) Companies that were mentioned at least once in a given story.
Institution (Primary Mention) The most prominently mentioned institution in a given story. Institutions are non-company organizations like government agencies, universities, etc.
Institutions (Any Mention) Institutions that were mentioned at least once in a given story. Institutions are non-company organizations like government agencies, universities, etc.
Person (Primary Mention) The most prominently mentioned person in a given story.
People (Any Mention) People that were mentioned at least once in a given story.
Keyword - Field Specific (Primary Mention)
The most prominently mentioned field specific keyword in a given story. Field specific keywords are terms and jargon related to specific disciplines extracted from the stories of a
network.
Keywords - Field Specific (Any Mention)
Field specific keywords that were mentioned at least once in a given story. Field specific keywords are terms and jargon related to specific disciplines extracted from the stories in
your network.
Location (Primary Mention) Top location mentioned by count
Locations (Any Mention) Locations that appeared in the article regardless of count
Other (Primary Mention) Top extracted term that did not fall into the above categories
Extracted Other (Any Mention) All other terms extracted that did not fall into the above categories
Topics List of topics article falls under as denoted by Quid's data partners
Sentiment Summary Overall categorization of sentiment of article (e.g. postive, neutral, or negative)
Sentiment Combined Score A numerical representation of how negative, neutral, or positive an article is. Scores range from -2 (most negative) to 2 (most positive).
Sentiment Positive Score A numerical representation of the intensity of positive sentiment in an article. Higher values represent greater positive intensity.
Sentiment Negative Score A numerical representation of the intensity of negative sentiment in an article. Greater negative values represent greater negative intensity.
Extracted Entity Latitude Latitude of where the article being published
Extracted Entity Longitude Longitude of where the article was published
Source Highest ranked source for a given story.
Source Publisher Name of the publishing company of source
Source Genre Areas/genre of publicationa
Source Category Main focus of the top ranked source for a given article (e.g. Consumer publication).
Source Rank A source’s rank represents the breadth of its readership. Rank 1 sources are most broadly read while rank 5 sources are the most local, interest-focused, or atypical.
Source Country Country where article was published
Source Sub Region Subregion where article was publsihed
Source Region Region where article was published
Sources Sources which published the article
Reporter Extracted name of report from article by-line

7. N EW S SPEC IFIC SEA R C H OPER ATOR S
Operator Example Description
title: title: "Apple-IBM partnership is a direct challenge to Microsoft" Search the article title
body: body: (oakland AND "social change") Search the article body
source: Apple AND source: NetworkWorld Search by the name of the source
sourceurl: sourceurl: "http://techcrunch.com" Search by the URL of the source. Requires quotes and leading http://
source_category:
source_category: Local or source_category: (Local OR
National) Narrow the search results by the source's category
published: published: 2016-01-01 or published >= 2016-01-01 Narrow the search results by the article's published date
country: travel AND country: Brazil Search by the country the article was published in
topic: topic: Business or topic: (Business OR Politics) Narrow the search results by an article's topics.
topicgroup:
topicgroup: Cloud Computing or topicgroup: (Cloud Computing
OR Cybersecurity) Narrow the search results by an article's subtopics
company: company: Spotify Search for articles referencing this company. Capitalization Matters!
company_medium:
company_medium: Quid or company_medium: (Quid OR Quid
Inc.) Search for articles with the company mentioned in the article relatively more frequently
company_high: company_high: Quid or company_high: (Quid OR Quid Inc.)
Search for articles that have the company prominently in the article, generally the primary
company mentioned in the article
person: person: "Nelson Mandela" Search for articles referencing this person. Capitalization Matters!
person_medium:
people_medium: Katy Perry
people_medium: (Katy Perry OR Justin Beiber) Search for articles with the person mentioned in the article relatively more frequently
person_high:
people_high: Katy Perry
people_high: (Katy Perry OR Justin Beiber)
Search for articles that have the person prominently in the article, generally the primary
person mentioned in the article
institution: institution: European Union Narrow the search results by all institutions extracted from the article's text
institution_mediu
m:
institution_medium: European Union
institution_medium: (European Union OR United Nations) Search for articles with the institution mentioned in the article relatively more frequently
institution_high:
institution_high: European Union
institution_high: (European Union OR United Nations)
Search for articles that have the institution prominently in the article, generally the primary
institution mentioned in the article
articletype: articletype: news or articletype: (news AND blogs) Narrow the search results by article type (blogs, news, ln-news)
blogrank:
(keyword: "design innovation") AND ((blogrank: 1) OR
(newsrank: 1) Search by the blogrank, which roughly correlates to readership (lower = more readers)
newsrank: (keyword: "design innovation") AND (newsrank: 1) Search by newsrank, which roughly correlates to readership (lower = more readers)
keywords: keyword: ("alternative medicine" OR "holistic health") Search by extracted keywords

8. N ETW OR K MA P A N D C LU STER D EEP D IVE
Quid’s network map is a robust way to get a bird’s eye view of your research topic with the ability to investigate
focused conversation areas with increased granularity
Network Map: Shows the overall search results – it is important to notice the relative size of each cluster (% volume) and the relative
spatial relationship to learn about the dominant and emerging categories, as well as the similarity or difference in language used to describe
them.
• View A: The nodes represent unique articles bundled into color-coded topic clusters discussing the future of the Smart Home.
• View B: ”Command – click” to select only the topic clusters which reference the “Smart Kitchen.” Clicking on the “Filter” button on the
Quid control panel, select “Highlight Mode” to show the presence of these select clusters against the de-luminated backdrop of the
entire network (high level view). This will allow us to explore how much of the general conversation focuses on the application of
connected devices in the kitchen.
• View C: In the control panel, we’ve changed the ”Color By” from ”Clusters” to “Sub-Clusters” to reveal a layer deeper to provide a
more detailed perspective of the 9 sub-topics discussing tomorrow’s kitchen (more granular view).
A B C
Curated network map view Focus on Smart Kitchen Sub-cluster perspective

9. EMPLOYIN G STR ATEGIC TA GGIN G
You can add a tag to any company, article, or patent in your network so that you can keep track of it as you
move from one view to the next. You can also use them to filter through noise and create your own coloring
scheme for your network.
Search in Network Tagging:
On the upper right corner of your screen, use the search
bar to search for keywords, companies, or people to see
how and where they map within the network. Select the
correct entity “Company (Primary mention)” or
“Company (Any Mention)” for companies, “People
(Primary mention)” for people, etc.
Selection Tagging:
You can also choose your tag by selecting several
different things you want to tag by selecting Command
or Ctrl and clicking on whatever you would like to tag.
Once you’ve selected the content you’re interested in
isolating, you can Tag it to refer to it later. Similarly, we
can tag an article we like and name it “read”.

10. ID EN TIFYIN G TR EN D S IN N EW S C OVER A GE
One of the key benefits of Quid is the ability to understand the larger conversation about your research topic
and how it changes over time.
Timeline: Enables you to see how particular conversations and themes are
changing in volume overtime – you’ll be able to recognize themes that are
event driven or evolving.
• Count of stories colored by clusters
• Count of clusters colored by company
• Social sharing of clusters colored by clusters
View A: Quid is tracking the volume of stories across a one year time period
with the nodes representing stories and colors representing topic clusters.
Quid has identified news coverage spikes in January and March which would
be interesting to investigate further.
View B: We have selected the “100% Stacked Bar” view in the control panel.
This will allow us to determine which topics are more evergreen versus those
that event-based.
• Investigating January’s spike, we observe significant increased
conversation in 3 areas: smart ovens (mauve), connected appliances
(orange), and home communication devices (light brown).
• Looking throughout the year, we observe that the topic of smart speaker
skills (yellow) has steady and consistent news coverage while the topic
of Alexa’s ”laughing problem” (green) is only present in March.
A
B

11. SU R FA C IN G IMPA C TFU L C ON TEN T
Social engagement is a powerful metric to determine the topics that truly resonate with the public
As you saw previously in Quid’s Timeline View, you can track volumetric changes in
areas of conversation. By simply changing the “Bar Value” from Number of Stories
to Social Engagement, you can easily identify the time and impact of particular
stories across your research topic.
Quid’s Social Engagement metric: Aggregate number of likes, comments, and
shares recorded for an individual article or topic.
In view A, all of the curated topics are represented in the timeline with particular
stories with the most social currency shown in the bar value.
• In February 2017, a story about Google Home (purple) raised a lot of
attention.
• In March 2018, there is an incredible spike (green) which is due to articles
discussing an issue with Amazon Alexa.
In view B, we were interested examining the coverage about Smart Speaker Skills
by selecting the topic in the cluster legend, then using our famous “Shift F”
keyboard shortcut to isolate and focus on the topic.
A
B

12. PU B LISH C OU N T VS. SOC IA L EN GA GEMEN T
A key insight for trendspotting is understanding the interplay between what the media pushes out compared to
what gains traction with readers engaging them to like, comment, or share the content
Scatterplot: This visualization is instrumental in
looking at up to 4 variables at once. The x-axis, y-
axis, and node color and size can all represent
different variables/metadata.
• Social engagement and published counts of
topic clusters
• Mapping sentiment of clusters by volume of
coverage
Here we are comparing the aggregate total of all
articles published and republished (Publish Count
(Sum)) and the average social sharing metric
(Social Engagement (Mean)).
When we divide this graph into quadrants, it
becomes evident about what topics the media writes
about and which one pique public interest.
• Topics such as “Connected cooking” and
”Voice commands” resonate with potential
consumers and they likely want more
coverage.
• While topics like “Virtual Assistants” is a
favorite subject matter, but perhaps readers
are too familiar with this and can be
deprioritized.
Control Panel: The navigation hub
within Quid, enabling the user to
pivot and analyze the data across
different views and lenses.
Information Panel: A dynamic panel that reflects general
information on any visualization within Quid. Key information such
as keywords, extracted entities, and statistics around social
engagement, published count, and sentiment can be found here.
Consumer/Media Sweet SpotContent Whitespace
Low Interest Areas Media Interest

13. N ETW OR K MA P A N D C LU STER D EEP D IVE
Quid’s network map is a robust way to get a bird’s eye view of your research topic with the ability to investigate
focused conversation areas with increased granularity
Bar Chart: Bar chart view is a standard way
to quantify Quid insights – with each axis
being customizable.
Having changed the “Color By” lens to
“Sentiment Summary,” we are able to analyze
the sentiment around key companies
mentioned throughout the news network that
we tagged earlier.
Though the coverage is mostly positive, you
can scan through the individual articles to
read more and gain the necessary context to
make informed decisions.

14. C R EATIN G A N EVOLU TION MA P
Use a Network Phases slide to show the progression of a media landscape over time in a more visually-
compelling way than the timeline view.
Insights to be Gained:
• What did the media landscape look like two years ago
compared to how it looks today?
• Have there been topics of conversation that have recently
appeared on the scene?
How to Get There:
• Start with your full network view
• Use the filter “Earliest Published” underneath Story
Attributes to select a timeframe to compare against
• Then use the slider to show a longer timeframe
• Finally, export to PowerPoint and use circles to identify the
major places of change within the network
Network Phases:

15. One of the most powerful preset views in Quid is the Scatterplot view, and it becomes even more powerful and
insightful when you can show change over time.
Insights to be Gained:
• What is interesting readers now that wasn’t interesting
them two years ago?
How to Get There:
• Using your control panel select “Scatterplot”
• Change nodes to represent Cluster
• Set the Y-Axis to represent Social Engagements (Mean)
• Turn Advanced Mode on to lock the axes
• Use the Earliest Published filter to look at only articles from
the first half of the period
• Export that image and turn the transparency down on the
image
• Create another Scatterplot image with articles from the
second half of the time period
• Export that image and lay it over the first scatterplot
• Add arrows to call out significant changes in social
resonance of topics
Overlapping Scatterplots
OVER LA PPIN G SC ATTER PLOTS

16. One helpful twist you can put on the standard timeline view is to color it by Source Quality, which can be one
indication as to how niche or mainstream a topic is.
Insights to be Gained:
• Is this issue becoming a more niche issue or more mainstream?
How to Get There:
• Using your control panel toggle to the timeline view
• Color by Source Quality
• To add in labels go to the “Labels” tab in the control panel and
turn labels on for bar segments
• Change to percentage
Source Quality Timeline View:
SOU R C E QU A LITY IN D IC ATOR S

17. When looking to give a detailed summary of each topics coverage over a long time period, a heat map can be
easier to assess in some use cases than a standard timeline.
Insights to be Gained:
• Were there pockets of time when a certain topic was
interesting?
• Relative to all other topics, which were the most-reported
on topics in a certain year?
How to Get There:
• After naming clusters, export all data to .CSV
• Create a pivot chart with clusters as rows, count of Node
IDs as values, and earliest published date as columns
• Then group the dates by months or years
• Then use conditional formatting to create a simple heat
map of the most-covered topics
News Heat Map
H EATMA P OF GR OW TH OVER TIME
Row Labels 2015 2016 2017 2018 Grand Total
Analyst Reactions 266 308 489 286 1,349
Stock News 120 128 212 294 754
Public Cloud 116 76 53 39 284
Groceries 26 50 165 42 283
Devices 53 57 73 91 274
Amazon
Headquarters
44 31 72 74 221
Revenue 58 84 48 30 220
Video Services 36 103 45 32 216
Amazon Prime
Increases
14 35 15 137 201
Retail 28 48 77 28 181
Freight Deliveries 13 103 24 17 157
Bezos News 7 27 103 9 146
Bookstores 2 114 5 1 122
Amazon Affiliates 49 26 29 17 121
Pharmacy News 1 0 58 58 117
Analyst Ratings 43 26 29 14 112
Google Ads 3 5 55 38 101
Prime Instant 72 20 2 1 95
CFO News 24 30 25 15 94