March, 2023
BeTrend – Building a Trend Aggregation Machine
Marcus Koring, Bertelsmann – Senior Director Technology & Project Development
Thorsten Liebig, derivo GmbH – Managing Director

2 March 23, 2023 · Bertelsmann TECH & DATA | derivo
Bertelsmann at a Glance
Media Investments
Services Education
Gütersloh
Headquarters
€18.7 Billion
Group revenues
€3,241 Million
Operating EBITDA
€2,310 Million
Group profit
145,027
Employees

3 March 23, 2023 · Bertelsmann TECH & DATA | derivo
Structuring AI use cases along our key value chains
Augmented Intelligence
AI for Media AI for Services
Content
Intelligence
Audience Intelligence
Service
Intelligence
Monetisation Monetisation
Tech Radar

4 March 23, 2023 · Bertelsmann TECH & DATA | derivo
All of our media companies try to identify trends
…
BEG Exam questions generation
G+J Leveraging Chefkoch recipe data
PRH - PRH India AI-based narrative analytics
PRH - PRH USA AI book generation
PRH - PRH USA Intelligent title tagging
RTL Group - RTLZWEI Cast engagement
RTL NL Content-based news recommen.
…
AI for Media
Content Intelligence Audience Intelligence
…
BMG Talent & trend identification
G+J Trend analysis/Topic prediction
PRH - PRH UK Trend identification
PRH - PRH USA Retail search data analysis
RTL Group - Fremantle Find drivers of TV ratings
RTL Group - RTL NL Predict trending topics
RTL Group - RTLZWEI Social media sentiment analysis
…
NLP
Metadata extraction
(Hyper)Personalization
Trend analysis
Pricing | Sales forecast.
…

5 March 23, 2023 · Bertelsmann TECH & DATA | derivo
Starting with a first use case
Use case “TV format ideation”
Provide semantically enriched social media and search data to help TV format/program ideation by:
1) identifying TV formats/shows trending elsewhere in the world and
2) detecting regional meta trends such as more conservative or liberal attitudes
Trending topics are a key driver for assessing potential content popularity (“popularity prediction”).
Editorial teams and creative units spend a lot of time on searching for good and relevant stories. AI can help to extract data
from various sources such as Twitter, Google Trends, Wikipedia, etc. and to process, combine, mix and analyse these data
points for producing a list of trending topics in such a way that it reveals promising new storylines, i.e., it helps to create a
better understanding of the best topics to address.
“As an editor of RTL BLVD, I want to know instantly what is happening in The Netherlands and in the world so I can create an
item as quickly as possible.”
“As Program Coordinator, I want to know what trends and sentiments do we see and to what extend they have an impact on
scheduling TV programs so we can adjust the schedule.”
“As Head of Creative Unit, I want to combine various trends so I can come up with a new creative concepts faster than the rest.”
“As Head of Content, I want to validate new creative formats.”

6 March 23, 2023 · Bertelsmann TECH & DATA | derivo

7 March 23, 2023 · Bertelsmann TECH & DATA | derivo
Disambiguation
Which „Meghan“ is trending?
Why is „Meghan“ trending?
What is the context?
Has the same „Meghan“ trended before
and why?

First Step
Took over the great initial prework
from RTL NL
Second Step
• Started to gather first data
• Entity linking based on wiki
• Started the legal assessment and
market research
Third Step
• Talked to several colleagues in the Data RTL
Mediengruppe, PRH, RTL II
• Added elastic search to search the database
Fifth Step
• API was way too slow
• Switched to Graph DB
• Rebuilding Data Pipeline
Fourth Step
• Built up a data pipeline
• Publishing the API
The Journey

9 March 23, 2023 · Bertelsmann TECH & DATA | derivo
Datapipeline
Collecting & Preparing Trends from Social Media to make them Graph-ready
Data Acquisition
+ …
Entity
Disambiguation
Trend Scoring
JSON
Aggregation &
Creation
Graph
Tools –
Model & Cleaning
+ … + …
Tools –
Google & TextRazor
Tools –
Scoring

10 March 23, 2023 · Bertelsmann TECH & DATA | derivo
From Tables to the BeTrend Graph & Applications
Data Acquisition
Entity
Disambiguation
Trend Scoring
JSON
Aggregation &
Creation
Graph
Power User / Developer
End User

11 March 23, 2023 · Bertelsmann TECH & DATA | derivo
Data Aggregation and Enrichment
Data Acquisition
Entity
Disambiguation
Trend Scoring
JSON
Aggregation &
Creation
• Temporal normalization (one JSON per day)
• Entity Recognition via external NLP service
• Trend word Classification (Wikidata, Freebase, IPTC, etc.)
• Gathering of related topics
Used technology:
• Azure table
storage
• SQL
• Python
• ext. Services via
REST
Graph

12 March 23, 2023 · Bertelsmann TECH & DATA | derivo
Data Aggregation and Enrichment
Data Acquisition
Entity
Disambiguation
Trend Scoring
JSON
Aggregation &
Creation
Graph
• Temporal normalization (one JSON per day)
• Entity Recognition via external NLP service
• Trend word Classification (Wikidata, Freebase, IPTC, etc.)
• Gathering of related topics
Used technology:
• Azure table
storage
• SQL
• Python
• ext. Services via
REST

13 March 23, 2023 · Bertelsmann TECH & DATA | derivo
Data Import and Graph Model
Data Acquisition
JSON
Aggregation &
Creation
Used technology:
• JSON Schema
• Drawings
• Cypher
• APOC
Graph
Graph Model Importer
• Key modelling dimensions:
-time (when, how often, consecutive?)
-location (country, continent)
-context (classification schemata)

14 March 23, 2023 · Bertelsmann TECH & DATA | derivo
Data Import and Graph Model
Data Acquisition
JSON
Aggregation &
Creation
Used technology:
• JSON Schema
• Drawings
• Cypher
• APOC
Graph
Graph Model Importer
• Key modelling dimensions:
-time (when, how often, consecutive?)
-location (country, continent)
-context (classification schemata)
UNWIND ['denmark', 'france', 'germany', …]
…
CALL apoc.load.json('./JSON Dumps/2022-12-01.json')
YIELD value
MERGE (i:Crawl …)
UNWIND … AS tword
MERGE (tw:Trendword {trendword: tword.trendword})
…
CASE WHEN entity.confidence <= 0.5 THEN
…
FOREACH …

15 March 23, 2023 · Bertelsmann TECH & DATA | derivo
Graph Meta Model

16 March 23, 2023 · Bertelsmann TECH & DATA | derivo
Demo

17 March 23, 2023 · Bertelsmann TECH & DATA | derivo
Lessons Learned & Outlook
Learnings:
• Graph technology is quick to use
• Neo4j on-premise and in the cloud
• Graph models are flexible + very well suited to reflect
the use case
Next:
• Professionalization the data import
• Improvement of Entity Recognition
• Refinement of the graph model
• Graph Data Science
Use-Cases AI for
Media
• Generative AI
• Recomendation