#supplychain and Twitter Analytics

1. Presented by: Dennis L. Kappen, Ph.D. Candidate, GAMER Lab, FBIT Twitter :3d_ideation Image Source: http://room214.com/social/getting-to-know-twitter-analytics
2. 12/12/2016 Dennis L. Kappen Twitter :3d_ideation 2 Image Source: http://cdn2.business2community.com/wp-content/uploads/2015/02/twitter-analytics.png.png Moving Product or Service > to Customer Supply Chain Management > SCM
3. 12/12/2016 Dennis L. Kappen Twitter :3d_ideation 3  Extracting intelligence  from 22,399 #supplychain tweets.  Framework  descriptive analytics (DA),  content analytics (CA) integrating text mining and sentiment analysis,  network analytics (NA)  Findings  Usage of supply chain tweets Image Source: http://cdn2.business2community.com/wp-content/uploads/2015/02/twitter-analytics.png.png
4. 12/12/2016 Dennis L. Kappen Twitter :3d_ideation 4  Introduction  Background  Framework  Research method (Results)  Discussion  Implications  Limitations and Future Work Image Source: https://media.licdn.com/mpr/mpr/AAEAAQAAAAAAAAQWAAAAJGE4ODJkOWY0LTVmYTctNDA5NC04ZDRkLWMwNWEwZWU5MzY1NQ.jpg
5. 12/12/2016 Dennis L. Kappen Twitter :3d_ideation 5
6. 12/12/2016 Dennis L. Kappen Twitter :3d_ideation 6  Slow growth in the usage of social media  Focus on:  product traceability data  geo-location and mapping data  supply chain visibility and intelligence Image Source: https://pharmapromotional.files.wordpress. com/2013/08/pharma_supply_chain_inform ation_flow.png
7. 12/12/2016 Dennis L. Kappen Twitter :3d_ideation 7  Access toTwitter data usingTwitter Application Programming Interface (API)  UnderstandTwitter within SCM context  Analyse: tweets, replies, and retweets Image Source: http://www.glitztechnology.com/images/twi tterapi-539x230.jpg
8. 12/12/2016 Dennis L. Kappen Twitter :3d_ideation 8  Research direction:  Tweet characteristics  Topics characteristics  User characteristics  Sentiment characteristics. Image Source: http://www.glitztechnology.com/images/twi tterapi-539x230.jpg
9. 12/12/2016 Dennis L. Kappen Twitter :3d_ideation 9  Twitter Firehoses are expensive  Analysis is expensive  Extract intelligence from the highly enriched and unstructured social media data
10. 12/12/2016 Dennis L. Kappen Twitter :3d_ideation 10  Framework:  Descriptive analytics (DA)  Content analytics (CA)  Network analytics (NA) Image Source: https://upload.wikimedia.org/wikipedia/com mons/b/b7/Lorimerlite_framework.JPG
11.  Descriptive analytics (DA)  Descriptive Statistics  Tweet metrics ▪ INTELLIGENCE extraction ▪ METRICS: number of tweets, word counts, @user per tweet, number of hashtags, etc. ▪ WHO tweets, replies, and retweets? ▪ WHICH URLs? 12/12/2016 Dennis L. Kappen Twitter :3d_ideation 11 Image Source: http://wersm.com/wp- content/uploads/2013/11/twitter-analytics- 657x360.jpg
12.  Content analytics (CA)  Texts are unstructured  Text cleaning and processing  Information and opinions  Advanced text mining: sentiment analysis  TwitterAnalytics relies on automatic text processing techniques and algorithms 12/12/2016 Dennis L. Kappen Twitter :3d_ideation 12 Image Source: http://www.contentanalyticsinc.com/about
13.  Network analytics (NA)  Users engage using@reply and retweet  Network topology ▪ Nodes (users) ▪ Edges (relationships)  Twitter data ▪ friendship network and @reply (or mention) network. 12/12/2016 Dennis L. Kappen Twitter :3d_ideation 13 Image Source: http://www.contentanalyticsinc.com/about
14.  Network analytics (NA)  Centrality analysis  Community analysis  Modularity analysis 12/12/2016 Dennis L. Kappen Twitter :3d_ideation 14 Image Source: http://www.contentanalyticsinc.com/about
18.  500 million tweets/day (2015)  #supplychain 22,399 tweets  Script languages  Tweet Statistics  Original tweets (58%)  Retweets (28%)  @replies (14%) 12/12/2016 Dennis L. Kappen Twitter :3d_ideation 18 Image Source: http://yogfitness.com/wp- content/uploads/2016/04/historical- tweets.jpg
19.  4313 unique users  Active users = original tweets + retweets+@replies  Visibility = @replies received + retweets received 12/12/2016 Dennis L. Kappen Twitter :3d_ideation 19 Image Source: http://yogfitness.com/wp- content/uploads/2016/04/historical- tweets.jpg
20. 12/12/2016 Dennis L. Kappen Twitter :3d_ideation 20  Active users = original tweets + retweets+@replies  Visibility = @replies received + retweets received
21. 12/12/2016 Dennis L. Kappen Twitter :3d_ideation 21  One or more URLs = 89%  Active andVisible users include URLs in their tweets  Top URLs include:  companies' webpages  SCM online newspaper sites, and  articles about manufacturing leadership,  and Big Data.
23. 12/12/2016 Dennis L. Kappen Twitter :3d_ideation 23  3839 different hashtags appear 62,575 times
25. 12/12/2016 Dennis L. Kappen Twitter :3d_ideation 25 Exemplar tweets Sentiment This development is pretty exciting for me #supplychain #sustainability 3 (Positive) Samsung assures excellent working conditions in China. 〈http://t.co/9VcBIL1XAj #workingconditions #supplychain #responsiblesourcing #rtw〉 3 (Positive) Loving the feedback that I am getting on this report today! #supplychain 〈http://t.co/7DJaJFFl〉 3 (Positive) As parked 787 s multiply? Boeing cash drain worries grow 〈http://t.co/up6V9sBYbi #supplychain〉 -3 (Negative) CHaINA Magazine: Apple Finds Child Labor Abuses in Its Supply Chain – As the controversy regarding. 〈http://t.co/kt6MMXvng #SupplyChain〉 -3 (Negative) Walmart US Chief Says Sales are Suffering Because of Restocking Issues 〈http://t.co/xhpK4pYy3v #supplychain〉 -3 (Negative)
26. 12/12/2016 Dennis L. Kappen Twitter :3d_ideation 26 Exemplar tweets Sentiment CSR Investigation of #Apple #SupplyChain by #SACOM reveals continued #labor abuse throughout despite Apple's claims. 〈http://t.co/Cm0VYeTT94 #CSR〉 Negative Loving this Green Supply Chain infographic 〈http://t.co/M0spSUs6WG #supplychain #CSR #infographic @SCInsightsLLC〉 Positive IT Supply Chain Disruption a MajorThreat to Business from @forbes 〈http://t.co/raX48u4Zcw #supplychain #IT #analytics〉 Negative How @Sony strengthened its supply chain and added value 〈http://t.co/B0DTiWPAnm #GrnBz via @GreenBiz #IT #supplychain〉 Positive Logistics Could I expect to receive my parcel today? I doubt it. Another terrible experience from @Parcel2Go #expensive #supplychain #logistics Negative Great day @ #DalhousieUniversity! Excellent case study presentations by 16 teams. Join Careers in Motion @JDIcareers #logistics #supplychain Positive Risk With new #supplychain controls in place IKEA meatballs back after horsemeat scare 〈http://t.co/YKvYT1ov1H #risk〉 Negative
27. 12/12/2016 Dennis L. Kappen Twitter :3d_ideation 27  5447 Nodes = users  9238 edges = relationships (@reply)  Network diameter = 19  Average path length =5.82 Example: 6 nodes, 7 edges : StackOverflow
28. 12/12/2016 Dennis L. Kappen Twitter :3d_ideation 28 Image Source: http://image.slidesharecdn.com/socialnetw orkanalysis-100225055227- phpapp02/95/social-network-analysis-19- 638.jpg?cb=1385568277  Degree Centrality  In-degree  Out-degree
29. 12/12/2016 Dennis L. Kappen Twitter :3d_ideation 29  CentralityAnalysis (node-level)  Nodes connectedness  Community Analysis (network-level)  Network was sparsely distributed  Over 400 communities in the #supplychain network Image Source: http://www.compjournalism.com/wp- content/uploads/2012/11/centrality- measures.png
30. 12/12/2016 Dennis L. Kappen Twitter :3d_ideation 30Companies Industry Press Individual users
31. 12/12/2016 Dennis L. Kappen Twitter :3d_ideation 31 (Blondel et al., 2008)
33. 12/12/2016 Dennis L. Kappen Twitter :3d_ideation 33 Image Source: http://www.supplychain247.com/images/arti cle/a_portrait_of_the_supply_chain_manag er_wide_image.jpg  Topical  73% of #supplychain tweets contained two or more hashtags  Factors for information diffusion
34. 12/12/2016 Dennis L. Kappen Twitter :3d_ideation 34 Image Source: http://blog.mytmc.com/wp- content/uploads/2015/01/ConnectPorts.jpg  Topic Areas  Popular hashtags  Topics of interest
35. 12/12/2016 Dennis L. Kappen Twitter :3d_ideation 35 Image Source: http://http://erau.edu/- /media/images/shared/degrees/videos/worldwide/ master-of-science-in-logistics-and-supply-chain- management-source.png  Characteristics of users  Job or career services  SCM practitioners  Trade magazines  Active users vsVisible users  Users vsTweets
36. 12/12/2016 Dennis L. Kappen Twitter :3d_ideation 36 Image Sourcehttp://beta.www.hull.ac.uk/editor- assets/images/Logistics/supply.png  #supplychain had low sentiment  Major events influencing sentiments  Promotional tweets  Negative sentiment
37. 12/12/2016 Dennis L. Kappen Twitter :3d_ideation 37 Image Sourcehttp://beta.www.hull.ac.uk/editor- assets/images/Logistics/supply.png
38. 12/12/2016 Dennis L. Kappen Twitter :3d_ideation 38  Short data collection period  #supplychain vs keywords  Future work  Guidelines  Impact of social media on supply chain performance
39. 12/12/2016 Dennis L. Kappen Twitter :3d_ideation 39  Research Methods section  Reporting Results  Themes in word analysis and sentiment analysis  No explanation of the evolution of these themes (inductive or deductive ?)
40. www.gamefulplay.ca Twitter :3d_ideation 12/12/2016 Dennis L. Kappen Twitter :3d_ideation 40 .com

Editor's Notes

Hello everybody, I am Dennis Kappen, and I am here to present the research paper on “Insights from hashtag #supplychain and Twitter Analytics: Considering Twitter and Twitter data for supply chain practice and research
At the onset let us establish that Supply Chain is a complex domain and involves a system of organizations, people, activities, information, and resources involved in moving a product or service from supplier to customer

Based on a report by Bernard Marr
Best-Selling Author, Keynote Speaker and Leading Business and Data Expert
“Supply chain management is a field where Big Data and analytics have obvious applications. Until recently, however, businesses have implemented big data analytics in areas of operation such as marketing or manufacturing easily than in Supply chain management.”
This is where this research paper fits in.
A few takeaways from this paper are:
To understand how the researchers extracted intelligence from 22,399 #supplychain tweets
To understand the Proposed framework for data analytics combining three methodologies namely:
descriptive analytics (DA), content analytics (CA) which integrated text mining and sentiment analysis, and network analytics (NA) relying on network visualization and metrics

Furthermore gathering insights on findings on the usage of #supplychain tweets which are shown to be used by different groups of supply chain professionals and organizations (e.g., news services, IT companies, logistic providers, manufacturers) for information sharing, hiring professionals, and communicating with stakeholders, among others

http://cdn2.business2community.com/wp-content/uploads/2015/02/twitter-analytics.png.png
https://media.licdn.com/mpr/mpr/AAEAAQAAAAAAAAdnAAAAJDlkYTdkNTQ0LTcyYWItNGIwMi1iOWRlLWI4OWIzOWMwODhlYQ.jpg
Lets follow through the sections of the paper as in the order of:
Introduction
Background
Framework
Research method (Results)
Discussion
Implications
Limitations and Future Work
We know that Social Media has been used for
Marketing
Brand Management
Product and Service Promotion
Customer engagement
Recruitment
Sales forecasting &
Education

At the same time Big Data has been extensively used for
stock price predictions
prevention of epidemics
early event monitoring
election predictions
crisis management
brand management
public relations
information diffusion
Public opinions and similar areas of application
Within this intersection of social media, big data and SCM there has been a slow growth in the usage of social media

From this image that I found, we can see that while the flow of physical goods can be unidirectional, the flow of information is bidirectional

And There has been a focus on using social media for product traceability data, geo-location and mapping and supply chain visibility and intelligence.
So the question is Why was Twitter used as medium for Data analytics in this research paper?

The main reasons as explained by the authors were
Due to open access to the Twitter API
To Understand the relevance of Twitter within the SCM context and
To Develop a framework for analyzing supply chain tweets of three types: tweets, replies and retweets
The authors set out to analyse supplychain tweets and look for relevance within these four attributes:

Tweet characteristics
Topics characteristics
User characteristics
Sentiment characteristics.
Through streaming using APIs, only 1% of publically available Twitter data can be acquired
Through companies like GNIP, DataSift, , which are known as Twitter Firehoses. 100% of Twitter data can be available through these data providers. This can prove to be quite expensive.

Analysis of collected data can become challenging because : Data are less structured because of the existence of ( texts, informal expressions) and are more enriched because of the presence of elements such as user profiles, follower information, hashtags, URL)

The use of diverse research methods and metrics are therefore necessary to extract intelligence from the highly enriched and unstructured social media data
Due to this reason, the authors propose a framework which comprises of
Descriptive analytics (DA)
Content analytics (CA) and
Network analytics (NA)

Lets look at these modules in detail in the next few slides
Descriptive analytics focuses on descriptive statistics, which relates to information such as such as the number of tweets, distribution of different types of tweets, and the number of hashtags.

Most of us have conducted surveys in our grad studies, and we can relate to this situation.

While a small number of metrics (e.g., sample size, response rate, responder profile) are used for the survey data, the enriched nature of Twitter data requires intelligence extraction, using a large set of metrics regarding tweets such as number of tweets, word counts, @user per tweet, number of hashtags

Knowing who tweets, replies, and retweets is important both for practitioners looking for business value from Twitter and researchers studying a phenomenon

a large portion of tweets contain one or more URLs in their texts. URLs could be news releases, reports, articles, and more. Thus, analyzing URLs can reveal what topical interests and informa-tion are considered important among Twitter users
Content analytics (CA), refers to a broad set of natural language processing (NLP) which is enabling computers to derive meaning from human or natural language input. Additionally the authors used text mining methods

Social media data are primarily texts and thus “unstructured” in
nature.

A Tweet's text is informal and composed of a short list of words, hashtags, URLs, and other information. Thus, a careful consideration of text cleaning and processing is a prerequisite for intelligence gathering.

A tweet may contain not just information, but also opinions

Hence advanced text mining techniques such as sentiment analysis may be key for extracting data.

Sentiment analysis is the process of computationally identifying and categorizing opinions expressed in a piece of text, especially in order to determine whether the writer's attitude towards a particular topic, product, or service , is positive, negative, or neutral.

Text mining transforms unstructured texts (or documents) into formatted data (or documents), using such techniques as tokenization, n-grams, stemming, and removing stop words (unnecessary words)

Those transformed texts can be used for text summarization, key word analysis, word frequency analysis, and text clustering, using machine learning algorithms, such as clustering and association analysis.

Not to be said:
tokenization is the process of breaking a stream of text up into words, phrases, symbols, or other meaningful elements called tokens.
n-gram is a contiguous sequence of n items from a given sequence of text or speech. The items can be phonemes, syllables, letters, words or base pairs according to the application. The n-grams typically are collected from a text or speech corpus
A phoneme /ˈfoʊniːm/ is one of the units of sound (or gesture in the case of sign languages, see chereme) that distinguish one word from another in a particular language. The difference in meaning between the English words kill and kiss is a result of the exchange of the phoneme /l/ for the phoneme /s/. Two words that differ in meaning through a contrast of a single phoneme form a minimal pair.
stemming is the process of reducing inflected (or sometimes derived) words to their word stem, base or root form
Twitter users engage through @reply and retweets which could lead to being able to extract network information from Twitter data using techniques and metrics in network theory

Nodes (which are Twitter users) and edges (which are the links or relationships between users ) are two basic terms in the theory. Network topology refers to a layout of the nodes and the edges based on the information of reply and retweet in Twitter. This network visualization uncovers patterns in interactions among users

With twitter data, the there could be two kinds of topological networks: which are friendship network and @reply (or mention) network.

Friendship networks can be constructed based on the information of follower and following. Also, the conversation using @reply creates interpersonal relationships among Twitter users.
Centrality (or popularity) analysis uses node-level metrics, such as degree and betweenness centrality, revealing influential actors in the network

Degree centrality, a key metric, explains who has the most ties (or degrees) to others in the network i.e. those nodes adjacent to a focal node, “betweenness centrality” includes distant paths of the focal node

While centrality analysis mainly focuses on individual nodes (or users in Twitter), community analysis explores network-level characteristics. For example, network density represents the portion of all possible connections between nodes, and, thus, it is a measure of network cohesion.

Modularity analysis identifies specific communities from the network through visualization. Modularity is a measure of how strongly the network is divided into modules
Descriptive analytics which includes Tweet metrics, User Metrics and URL metrics
Content analytics includes word analysis, hashtag analysis and sentiment analysis
Network analytics includes Topological analysis , Centrality analysis and Community analysis.
Let us know look at the results from using the above framework:

The authors initially conducted a series of keyword and hashtag searches, including “supply chain”, “SCM”,
“logistics”, “supply chain management”, and so on. Which led them to find out that #supplychain was the most prevalent hashtag used by supply chain professionals and organizations

To perform descriptive analytics , the authors used script languages (Bruns and Burgess, 2011) and multiple statistical and data mining techniques.

Script languages were needed to extract information such as users and hashtags from the dataset of tweets
Active users are calculated
based on the number of tweets (which are original tweets + retweets + @replies).

4313 unique users were seen within the dataset

The visibility of users can be calculated by the number of @replies
received ( which is = @replies received+ retweets received)
This slide shows the most active users and the most visible users: the most active users are not necessarily the most visible users.

The authors also visualized the activity of the most visible users. The result also suggests that highly visible users tend to be active users as well
It is interesting to note that

89% (19,890) of the total tweets contained one or more URLs.

11,362 different URLs were found from these tweets. Especially active and visible users include URLs in their tweets: almost
From the word analysis perspective:

The most popular words in tweets were logistics
(found in 3961 tweets), jobs (3874), manufacturing (1309), careers (1160), risk (744), procurement (719), warehouse (614), software (605), sustainability (602), import (549), export (544), freight (526), sourcing (525), global (515), operations (503), retail (470), cloud (314), data (276), re-shoring (276), visibility (238), and security
(219), among others.

An important aspect of this table is the clustering of tweets into five popular themes –which were logistics, sustainability, manufacturing, risk, and software

However my concern is that the authors did not explain, or I may have missed this , that they do not explain how they deduced these five themes.
This is a collection of popular hashtags that were evident in the hashtag analysis
Before we look at sentiment analysis, Just to Recap:
Sentiment analysis: is the process of computationally identifying and categorizing opinions expressed in a piece of text, especially in order to determine whether the writer's attitude towards a particular topic, product, etc., is positive, negative, or neutral.
The authors chose SentiStrength because the sentiment analysis algorithm is designed for informal texts such as tweets

Fig 3. shows the sentiments at the entire dataset level. Many tweets appear to be neutral (neither positive nor negative), as indicated by a high portion (67%) of tweets with the score 0.

Another large portion (28%) of tweets scores either -1 or +1, which is relatively neutral. However, some tweets contain either strongly negative or positive sentiment.

Figure 4 shows the tweets separated into five clusters, using such themes as corporate social responsibility (CSR), Risk, Logistics, Manufacturing, and IT.

Again my concern being a missing rational for how these themes were evolved and why does this not sync with the earlier themes…
This table shows some exemplar tweets with relatively strong sentiments.

Some examples were ….
This table shows a few examples from each group or cluster

Some examples were…
From the Network Analytics perspective, topological analysis revealed
5447 Nodes (users) = which are users who sent/received

9238 Edges or connection links are relationships between those users through @reply

Once the shortest path length from every node to all other nodes is calculated, the diameter is the longest of all the calculated shortest paths in a network

I found this image on Stackoverflow which illustrates a network with 6 nodes and 7 edges or links…

The authors indicate that the Average path length was 5.82 meaning that everyone is about 6 nodes away from each length
From a network analytics from a centrality analysis perspective, Just to recap: Centrality analysis is a simple measure of a node's connectedness with others and can be an indicator of a user's popularity.

Degree centrality, a key metric, explains who has the most ties (or degrees) to others in the network

From this image that I found, you can see that nodes 3 and 4 have degree 4

In-degree means number of links that lead into a node; out degree indicates out of the node…
Furthermore, This is another image to explain highest number of edges, The node marked “RED” has the highest degree centrality

Closeness centrality is the lowest average shortest distance to all other nodes.

I have purposely placed these slides here so that we could review a table in the next slide
Where Based on this rationale, the analysis shows that Companies (like ,@deloitte,@toyotaequipment)and industry presses (e.g.,@ILmagazine) in the SCM field draw many replies or mentions, as indicated by their high in-degree values. Some individual users (like ,@Lcecere) are noticeably active users.

While centrality is low, analysis shows the presence of supply chain companies, logistics providers and retailers
The authors refer to Bloden et al. and I looked up This image from this paper for Modularity, which is essentially one measure of the structure of networks or graphs. They had defined a greedy algorithm for determining modularity

It was designed to measure the strength of division of a network into modules (also called groups, clusters or communities). Networks with high modularity have dense connections between the nodes within modules but sparse connections between nodes in different modules.

Modularity is often used in optimization methods for detecting community structure in networks. )

(Each pass is made of two phases:
one where modularity is optimized by allowing only local changes of communities;
one where the found communities are aggregated in order to build a new network of
communities. The passes are repeated iteratively until no increase of modularity is
possible.)

https://en.wikipedia.org/wiki/Modularity_(networks)

https://en.wikipedia.org/wiki/Modularity_(networks)
Based on this protocol, (which in IMHO is also not detailed) the ommunity analysis showed a very low graph density (0.001), indicating that the entire #supplychain network was sparsely distributed (not cohesive)

The modularity-analysis revealed over 400 communities in the #supplychain network. A majority of these communities are quite small-scale.

There were four large communities, each of which represents over 5% of the total nodes. These four communities were about corporate social responsibility, sustainability, manufacturing, and SCM

This I like because there was a rationale on how they deduced the four communities…
In the discussion section the paper tries to link back to the four attributes of their research question indicated in the introduction section which were namely : Tweet characteristics, Topics characteristics, User characteristics and Sentiment characteristics.

From the tweet Characteristics perspective, supply chain tweets were conversational and engaging

The rate of hashtags in #supplychain tweets also draws attention. User inclusion of hashtags indicates “topical” relevance

The two factors that played a key role in determining information diffusion of supply chain :
1)Tweets were about:
Timely issues
And Challenges,
Job-related tweets were the least retweeted
2) Second, the number of hashtags were positively associated with the degree of diffusion through retweets.
Those tweets widely diffused through retweets tend to contain six hashtags on average, which is twice that of other less popular tweets.
Under the section, the topics indicated were :logistics, manufacturing, sustainability, corporate social responsibility (CSR), risk management, and IT

Popular hashtags were: #manufacturing, #logistics, #sustainability,
#procurement, #csr, #3PL, #sourcing, #IT, #distribution, #transportation,
#risk, #healthcare, #ERP, #riskmanagement, #innovation,
#purchasing, #lean, and #inventory

topics of interest in tweets were
#BigData, #SocialMedia, #careers, #equity, #CRM, #SaaS, #humanrights, #horsemeat, #fashion, #RFID, #fairtrade, #ClimateChange, #Android, #ethics, #analytics, and #humantrafﬁcking

#humanrights
Interestingly some active users belonged to job or career services, SCM practitioners, trade magazines

Not all active users are highly visible. Visible users receive many @reply
(or mentions) and their tweets are retweeted.

Analysis also shows that a relatively small percentage of
Users account for a large portion of tweets.
Only 1% (45 users) and next top 9%
(395 users) account for almost 40% and 34% of tweets, respectively
#supplychain tweets contained relatively low sentiment. Descriptive analytics and Content Analytics helped to explain that Most #supplychain tweets were about events, SCM news and reports, jobs, and advertisements.

SCM-related incident during the period of data collection influence sentiments. Major events, such as the Bangladesh garment factory
collapse, draw emotional, largely negative, tweets. However this event happened after the end of data collection for this research study.

Positive sentiment was found in promotional tweets; as an example “(“How @Sony strengthened its supply chain and added value “

Risk-related tweets tend to carry far more
negative sentiment than those of other topics. There appear to be
disproportionately more negative tweets in the topic of manufactur-
ing.
This is a table that identifies implications of Twitter usage within the SCM context.

From a professional use perspective twitter usage is relevant for learning, promoting an networking

Organizational use serves for usage as a communication platform, to spread positive images through tweets and retweets

Additionally, #supplychain twitter usage information can serve as a hiring tool , as a sales channel, for market sensing and for sensing supply-chain related events.
The relatively short duration of data collection, which lasted a little over two months, is a limitation

The authors used hashtag #supplychain to search and collect Twitter
data. Another, perhaps better, approach would be using multiple
keywords (e.g., supply chain) and/or hashtags (e.g., #manufacturing,
#logistics) in data collection. This approach would enable research
using a large quantity of supply chain-related Twitter data.

developing detailed, practical guidelines, could help companies in designing industry applications, using Twitter and other social media platforms, for diverse supply chain activities, including new product
development, stakeholder engagement, supply chain risk management,
and market sensing

The other area is studying the impact of social media (and big data) investment (e.g., technologies, data scientists, and data repositories) on supply chain performance.
IMHO The research method section should have defined a set procedure on how they carried out their analysis. I.e. they just link to past papers and do not list the algorithm and how it was used. This was unclear

This leads to lack of clarity of the analysis method raising the issue of reliability and repeatability of their method.

Results are reported within the Research method section leading to confusion

Themes from Word analysis does not match the themes from Sentiment analysis : Why does this happen and why is there no explanation of this differentiation?
Thank you for listening and I look forward to your questions and discussions

#supplychain and Twitter Analytics

You are reading a preview.

Check these out next

#supplychain and Twitter Analytics

More Related Content

Similar to #supplychain and Twitter Analytics (20)

More from Dennis Kappen (7)

Recently uploaded (20)

#supplychain and Twitter Analytics

Editor's Notes

#supplychain and Twitter Analytics

You are reading a preview.

#supplychain and Twitter Analytics

Recommended

More Related Content

Similar to #supplychain and Twitter Analytics (20)

More from Dennis Kappen (7)

Recently uploaded (20)

#supplychain and Twitter Analytics

Editor's Notes