There are lots of tools emerging that appear to give us wonderful statistics and data about Twitter and it’s hard to know which data we actually want and how we want to receive it.
As Twitter's API has been undergoing a few changes recently, we wanted to give an overview of the information that you can still get from the platform itself and then provide some guidance on the best way to measure the data.
There are four main areas of Twitter data:
1. User data - relates to the user who posted the message.
2. Friend and follower data - relates to the relationship a user has to other users.
3. Tweet data - all the details and content relating to a particular tweet.
4. Places and Geographic data - the geographic and location based aspects relating to a person or twee.
To measure the data there are also four main measurements that we use to understand the impact of activities on Twitter:
1. Impressions - aggregated users exposed to messages.
2. Reach - number of unique users exposed to a message.
3. Frequency - number of times each unique user reached is exposed to a message.
4. Relevancy - reach to specific demographics.
When it comes to the ROI of these messages it's important to think about how they compare to your other channels in terms of reach and impressions.
Take a look at the presentation below - we hope it helps to reveal some of the Twitter data you can access and ways in which you might go about measuring it.
This presentation covers utilizing Social Media to grow your business. Topics include branding, engagement and interaction with prospective customers and brand advocates.
This presentation covers utilizing Social Media to grow your business. Topics include branding, engagement and interaction with prospective customers and brand advocates.
Introduction to the Responsible Use of Social Media Monitoring and SOCMINT ToolsMike Kujawski
These are my slides from a custom tool-based demonstration workshop I was asked to do where I went over various free tools that can be used to obtain valuable public data.
Hear about the buzz but have no idea how to start? This is a crash course will help you understand why you should care about Twitter and how to get started.
Studying user footprints in different online social networksIIIT Hyderabad
With the growing popularity and usage of online social media services, people now have accounts (some times several) on multiple and diverse services like Facebook, LinkedIn, Twitter and YouTube. Publicly available information can be used to create a digital footprint of any user using these social media services. Generating such digital footprints can be very useful for personalization, profile management, detecting malicious behavior of users. A very important application of analyzing users’ online digital footprints is to protect users from potential privacy and security risks arising from the huge publicly available user information. We extracted information about user identities on different social networks through Social Graph API, FriendFeed, and Profilactic; we collated our own dataset to create the digital footprints of the users. We used username, display name, description, location, profile image, and number of connections to generate the digital footprints of the user. We applied context specific techniques (e.g. Jaro Winkler
similarity, Wordnet based ontologies) to measure the similarity of the user profiles on different social networks. We specifically focused on Twitter and LinkedIn. In this paper, we present the analysis and results from applying automated classifiers for
disambiguating profiles belonging to the same user from different social networks UserID and Name were found to be the most discriminative features for disambiguating user profiles. Using the most promising set of features and similarity metrics, we
achieved accuracy, precision and recall of 98%, 99%, and 96%, respectively.
Credibility Ranking of Tweets during High Impact EventsIIIT Hyderabad
Twitter has evolved from being a conversation or opinion sharing medium among friends into a platform to share and disseminate information about current events. Events in the real world create a corresponding spur of posts (tweets) on Twitter. Not all content posted on Twitter is trustworthy or useful in providing information about the event. In this paper, we analyzed the credibility of information in tweets corresponding to fourteen high impact news events of 2011 around the globe. From the data we analyzed, on average 30% of total tweets posted about an event contained situational information about the event while 14% was spam. Only 17% of the total tweets posted about the event contained situational awareness information that was credible. Using regression analysis, we identified the important con- tent and sourced based features, which can predict the credibility of information in a tweet. Prominent content based features were number of unique characters, swear words, pronouns, and emoticons in a tweet, and user based features like the number of followers and length of username. We adopted a supervised machine learning and relevance feedback approach using the above features, to rank tweets according to their credibility score. The performance of our ranking algorithm significantly enhanced when we applied re-ranking strategy. Results show that extraction of credible information from Twitter can be automated with high confidence.
What Sets Verified Users apart? Insights Into, Analysis of and Prediction of ...IIIT Hyderabad
Social network and publishing platforms, such as Twitter, support the concept of verification. Veri-
fied accounts are deemed worthy of platform-wide public interest and are separately authenticated by the platform itself. There have been repeated assertions by these platforms about verification not being tan-
tamount to endorsement. However, a significant body of prior work suggests that possessing a verified
status symbolizes enhanced credibility in the eyes of the platform audience. As a result, such a station
is highly coveted among public figures and influencers. Hence, we attempt to characterize the network
of verified users on Twitter and compare the results to similar analyses performed for the entire Twit-
ter network. We extracted the whole graph of verified users on Twitter (as of July 2018) and obtained
231,246 English user-profiles and 79,213,811 connections. Subsequently, in the network analysis, we
found that the sub-graph of verified users mirrors the full Twitter users graph in some aspects, such as
possessing a short diameter. However, our findings contrast with earlier results on multiple fronts, such
as the possession of a power-law out-degree distribution, slight dissortativity, and a significantly higher
reciprocity rate, as elucidated in the paper. Moreover, we attempt to gauge the presence of salient com-
ponents within this sub-graph and detect the absence of homophily with respect to popularity, which
again is in stark contrast to the full Twitter graph. Finally, we demonstrate stationarity in the time series
of verified user activity levels.
It is in this backdrop that we attempt to deconstruct the extent to which Twitter’s verification policy
mingles the notions of authenticity and authority. To this end, we seek to unravel the aspects of a user’s
profile, which likely engender or preclude verification. The aim of the paper is two-fold: First, we test
if discerning the verification status of a handle from profile metadata and content features is feasible.
Second, we unravel the characteristics which have the most significant bearing on a handle’s verification
status. We augmented our dataset with all the 494 million tweets of the aforementioned users over a one
year collection period along with their temporal social reach and activity characteristics. Our proposed
models are able to reliably identify verification status (Area under curve AUC > 99%). We show that
the number of public list memberships, presence of neutral sentiment in tweets and an authoritative
language style are the most pertinent predictors of verification status.
To the best of our knowledge, this work represents the first quantitative attempt at characterizing
verified users on Twitter and also the first attempt at discerning and classifying verification worthy users
on Twitter.
Inferring social media user attributes using language and network informationNikolaos Aletras
Inferring attributes of social media users is an important problem in computational social science. Automated inference of user characteristics has applications in personalised recommender systems, targeted computational advertising and online political campaigning. In this talk, I’ll present how we can combine language and network information to predict users’ (1) socioeconomic characteristics (e.g., income and occupational class); and (2) voting behaviour.
Lancaster University - Jan 2019
Introduction to the Responsible Use of Social Media Monitoring and SOCMINT ToolsMike Kujawski
These are my slides from a custom tool-based demonstration workshop I was asked to do where I went over various free tools that can be used to obtain valuable public data.
Hear about the buzz but have no idea how to start? This is a crash course will help you understand why you should care about Twitter and how to get started.
Studying user footprints in different online social networksIIIT Hyderabad
With the growing popularity and usage of online social media services, people now have accounts (some times several) on multiple and diverse services like Facebook, LinkedIn, Twitter and YouTube. Publicly available information can be used to create a digital footprint of any user using these social media services. Generating such digital footprints can be very useful for personalization, profile management, detecting malicious behavior of users. A very important application of analyzing users’ online digital footprints is to protect users from potential privacy and security risks arising from the huge publicly available user information. We extracted information about user identities on different social networks through Social Graph API, FriendFeed, and Profilactic; we collated our own dataset to create the digital footprints of the users. We used username, display name, description, location, profile image, and number of connections to generate the digital footprints of the user. We applied context specific techniques (e.g. Jaro Winkler
similarity, Wordnet based ontologies) to measure the similarity of the user profiles on different social networks. We specifically focused on Twitter and LinkedIn. In this paper, we present the analysis and results from applying automated classifiers for
disambiguating profiles belonging to the same user from different social networks UserID and Name were found to be the most discriminative features for disambiguating user profiles. Using the most promising set of features and similarity metrics, we
achieved accuracy, precision and recall of 98%, 99%, and 96%, respectively.
Credibility Ranking of Tweets during High Impact EventsIIIT Hyderabad
Twitter has evolved from being a conversation or opinion sharing medium among friends into a platform to share and disseminate information about current events. Events in the real world create a corresponding spur of posts (tweets) on Twitter. Not all content posted on Twitter is trustworthy or useful in providing information about the event. In this paper, we analyzed the credibility of information in tweets corresponding to fourteen high impact news events of 2011 around the globe. From the data we analyzed, on average 30% of total tweets posted about an event contained situational information about the event while 14% was spam. Only 17% of the total tweets posted about the event contained situational awareness information that was credible. Using regression analysis, we identified the important con- tent and sourced based features, which can predict the credibility of information in a tweet. Prominent content based features were number of unique characters, swear words, pronouns, and emoticons in a tweet, and user based features like the number of followers and length of username. We adopted a supervised machine learning and relevance feedback approach using the above features, to rank tweets according to their credibility score. The performance of our ranking algorithm significantly enhanced when we applied re-ranking strategy. Results show that extraction of credible information from Twitter can be automated with high confidence.
What Sets Verified Users apart? Insights Into, Analysis of and Prediction of ...IIIT Hyderabad
Social network and publishing platforms, such as Twitter, support the concept of verification. Veri-
fied accounts are deemed worthy of platform-wide public interest and are separately authenticated by the platform itself. There have been repeated assertions by these platforms about verification not being tan-
tamount to endorsement. However, a significant body of prior work suggests that possessing a verified
status symbolizes enhanced credibility in the eyes of the platform audience. As a result, such a station
is highly coveted among public figures and influencers. Hence, we attempt to characterize the network
of verified users on Twitter and compare the results to similar analyses performed for the entire Twit-
ter network. We extracted the whole graph of verified users on Twitter (as of July 2018) and obtained
231,246 English user-profiles and 79,213,811 connections. Subsequently, in the network analysis, we
found that the sub-graph of verified users mirrors the full Twitter users graph in some aspects, such as
possessing a short diameter. However, our findings contrast with earlier results on multiple fronts, such
as the possession of a power-law out-degree distribution, slight dissortativity, and a significantly higher
reciprocity rate, as elucidated in the paper. Moreover, we attempt to gauge the presence of salient com-
ponents within this sub-graph and detect the absence of homophily with respect to popularity, which
again is in stark contrast to the full Twitter graph. Finally, we demonstrate stationarity in the time series
of verified user activity levels.
It is in this backdrop that we attempt to deconstruct the extent to which Twitter’s verification policy
mingles the notions of authenticity and authority. To this end, we seek to unravel the aspects of a user’s
profile, which likely engender or preclude verification. The aim of the paper is two-fold: First, we test
if discerning the verification status of a handle from profile metadata and content features is feasible.
Second, we unravel the characteristics which have the most significant bearing on a handle’s verification
status. We augmented our dataset with all the 494 million tweets of the aforementioned users over a one
year collection period along with their temporal social reach and activity characteristics. Our proposed
models are able to reliably identify verification status (Area under curve AUC > 99%). We show that
the number of public list memberships, presence of neutral sentiment in tweets and an authoritative
language style are the most pertinent predictors of verification status.
To the best of our knowledge, this work represents the first quantitative attempt at characterizing
verified users on Twitter and also the first attempt at discerning and classifying verification worthy users
on Twitter.
Inferring social media user attributes using language and network informationNikolaos Aletras
Inferring attributes of social media users is an important problem in computational social science. Automated inference of user characteristics has applications in personalised recommender systems, targeted computational advertising and online political campaigning. In this talk, I’ll present how we can combine language and network information to predict users’ (1) socioeconomic characteristics (e.g., income and occupational class); and (2) voting behaviour.
Lancaster University - Jan 2019
Virgin Media social media case study. Social media agency FreshNetworks show how they created an online community for gaining customer feedback and insight.
Social media case study: RBS Insurance (Devitt)Triptease
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Presentation used at a workshop at the Call Centre and Customer Strategy Conference, September 2009.
Presents a range of examples of good and bad use of social media in customer service: Zappos, Dell, Virgin Trains, United Airlines.
Affordable Stationery Printing Services in Jaipur | Navpack n PrintNavpack & Print
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At Techbox Square, in Singapore, we're not just creative web designers and developers, we're the driving force behind your brand identity. Contact us today.
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Have you ever heard that user-generated content or video testimonials can take your brand to the next level? We will explore how you can effectively use video testimonials to leverage and boost your sales, content strategy, and increase your CRM data.🤯
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[Note: This is a partial preview. To download this presentation, visit:
https://www.oeconsulting.com.sg/training-presentations]
Sustainability has become an increasingly critical topic as the world recognizes the need to protect our planet and its resources for future generations. Sustainability means meeting our current needs without compromising the ability of future generations to meet theirs. It involves long-term planning and consideration of the consequences of our actions. The goal is to create strategies that ensure the long-term viability of People, Planet, and Profit.
Leading companies such as Nike, Toyota, and Siemens are prioritizing sustainable innovation in their business models, setting an example for others to follow. In this Sustainability training presentation, you will learn key concepts, principles, and practices of sustainability applicable across industries. This training aims to create awareness and educate employees, senior executives, consultants, and other key stakeholders, including investors, policymakers, and supply chain partners, on the importance and implementation of sustainability.
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1. Develop a comprehensive understanding of the fundamental principles and concepts that form the foundation of sustainability within corporate environments.
2. Explore the sustainability implementation model, focusing on effective measures and reporting strategies to track and communicate sustainability efforts.
3. Identify and define best practices and critical success factors essential for achieving sustainability goals within organizations.
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1. Introduction and Key Concepts of Sustainability
2. Principles and Practices of Sustainability
3. Measures and Reporting in Sustainability
4. Sustainability Implementation & Best Practices
To download the complete presentation, visit: https://www.oeconsulting.com.sg/training-presentations
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3. Data on Twitter
User data: Users are at the centre of everything on Twitter: they follow,
favourite, tweet and re-tweet.
Friend & Follower data: Users follow each other through both
one-way and mutual following relationships on Twitter.
Tweet data: Tweets are the atomic building blocks of Twitter; 140 -
character status updates with additional associated metadata. People
tweet for a variety of reasons about a multitude of topics.
Places & GeoUser data: Attach a place or location to tweets, or
discover the location of tweets from all around the world.
www.freshnetworks.com
4. User data
Use the Twitter API to find out:
| User ID | Screename | Username | Favourites | Account restrictions | Biography | Picture | URL | Location
Return user data
information for up to 100
123213213
users, specified by either
user ID, screen name, or a
123213213
combination of the two. 123213213
123213213
Access the profile Run a search for users, similar
image in various sizes. to the ‘Find People’ button on
Twitter.com.
If you return extended user data information for a given user through the API,
(specified by ID) the author's most recent status (tweet) will be returned inline.
www.freshnetworks.com
5. Friend and follower data
Use the Twitter API to find out:
Followers | Following | IF following | IF not following | Relationship to | Multiple relationships to
IDs for every user IDs for every user the
following the specified user. Allows you to specified user is following.
test for the
existence of
friendship and
details about
the relationship
between two
users.
If you send 100 screen names or
user IDs in a csv file to the Twitter
API it will tell you the relationship to
the authenticated user (ie, follower,
following, no relationship).
www.freshnetworks.com
6. Tweet data
User the Twitter API to find out:
Tweet ID | Tweet content | Tweet creation date | ID of original tweet | Screen name & ID of original author
Show user ids for up to 100 users
who retweeted a status.
123213213 123213213 123213213 123213213
Search up to 100 of the
first retweets for a A single status, specified by the id
given tweet. parameter, will return the author’s most
recent status (tweet) inline.
123213213
www.freshnetworks.com
7. Places and GEOuser data
Use the Twitter API to find out:
Selected Language | Tweet coordinates | Type of place | Place name | Country |
Return information about a known
place or location.
Search for trends via
place names.
Search for
tweets near a
place.
www.freshnetworks.com
8. Limitations of the API
• Twitter only stores 6-9 days worth of tweets so historical search is
limited.
• A very small percentage of tweets are accurately geo-coded,
(estimation is about 1%).
• 150 unauthenticated (when not signed in) data request calls are
permitted per hour to the API. 350 authenticated calls (when signed
in) are permitted per hour.
• A search can only return 1500 results at a time.
NB: Most free tools that can be used to measure Twitter can access all
or parts of this data but usually fall victim to the API limitations
www.freshnetworks.com
10. Measuring Twitter - Impressions
• The aggregated number of followers that
have been exposed to a brand/message.
A B C D E
• This is a top line measurement which does
not take into account individuals seeing
multiple messages.
• The danger of this metric is that it does not
X highlight if a message has spread across a
narrow or wide audience.
Impressions = 5
www.freshnetworks.com
11. Measuring Twitter - Reach
C
• The total number of unique users exposed
to a message/brand.
D
A
• If an individual sees a company message
E
twice from different sources, the reach
F
figure is still only one.
B
G
Impressions = 8
H Reach = 6
www.freshnetworks.com
12. Measuring Twitter - Frequency
D
• The number of times each unique user
E
reached is exposed to a message.
A
F
• Frequency is important as it increases the
G likelihood of message retention by a user.
B
H
Impressions = 12
I
C
Reach = 8
J Frequency = 1.5
K
www.freshnetworks.com
13. Measuring Twitter - Relevance
• Whilst it is great to reach a high
number of people, if the message is of
no interest to them, it will not resonate.
• It is therefore essential to reach the
Target specific
demographics right people with the appropriate
message – be relevant.
• Measure reach to specific
demographics and then, if necessary,
re-target your campaign.
www.freshnetworks.com
14. Some initial tools to consider
Tweet Reach - http://tweetreach.com/
Useful tool with a simple user interface. Provides reach and impression data.
Twitter Analyser - http://www.twitteranalyzer.com/
For reviewing Twitter accounts. Can show reach, follower growth, follower
activity and more.
Tweet Grader - http://tweet.grader.com/
Find out specifics about people you intend to target and how the site ‘grades’
them.
Twitalyzer - http://twitalyzer.com
Useful for comparing tweets. Could be used for industry benchmarking.
www.freshnetworks.com
15. ROI
While you may have a high number of ‘followers’ on Twitter, it is important to
clearly demonstrate your Return On Investment (ROI) in measures that are
comparable to other marketing disciplines.
www.jenders.com/blog/2011/04/25/calculate-social-media-roi-through-
impressions/
www.freshnetworks.com
18. About us
FreshNetworks is a pure-play John Fell – Account
social media agency. Manager
We help global brands, like john.fell@freshnetworks.com
Telefónica, Allianz and Jimmy
Choo, engage customers,
develop advocacy and drive
sales.
Our strategies blend creative
Richard Dalke – Account
concepts with technical
Manager
deployment, and the metrics,
framework and key performance richard.dalke@freshnetworks.com
indicators we set help businesses
get measurable value from social
media.