Category Archives: Social Media

Posts about Social Media

Communication during the Coronavirus (I): Thematic analysis in Spanish digital news media

While it is obvious that the priority during this pandemic is to cure the sick, to prevent new cases from surfacing and to ensure there are economic and social measures in place to help the people and businesses most afflicted overcome the current situation; without a doubt, in the near future, the analysis of content related to the coronavirus that has been generated by the media and social network users will be the object of research for numerous disciplines such as sociology, philology, linguistics, audio-visual communication, and politics, to name a few.

At MeaningCloud we want to do our bit in this area, by applying our experience and our Text Analytics solutions to analyze the enormous volume of information in natural language, in Spanish and in other languages, in Spain and in other countries, given that, unfortunately, this is a global crisis.

This first article in the series centers on the thematic analysis of content that has been generated in Spanish by digital media platforms in Spain over the last month, how it has evolved during this period of time and the informative positioning of the main media platforms in Spain.

These other articles (only available, at the moment, in Spanish) analyse conversation topics on Twitter in Spain (both from the hashtags and general topics perspective and also applying a specific thematic categorization) and the linguistic analysis of presidential speeches related to this crisis.

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Pharmacovigilance: Monitoring the Voice of the Patient

Pharmacovigilance: Voice of the Patient

For the pharmaceutical industry, it is essential to listen and understand the feedback that their current and potential patients communicate through all sorts of channels and touchpoints.

Although there is a protocol that requires any identified Adverse Drug Reactions (ADRs) to be disclosed to the authorities, only 5–20% of them are reported. Fortunately, discussions regarding drugs, symptoms, conditions, and diseases can be analyzed to learn more about said branches of pharmaceutics. Artificial Intelligence significantly contributes in monitoring adverse episodes and understanding their impact in every phase of development.

Patient narratives of medicines and their adverse effects on social media represent an extra data source for drug safety monitoring.

At MeaningCloud, we have developed a platform to automate the process of monitoring ADRs on social media.

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Active social listening to protect Online Reputation (Part 2)

EDITOR’S NOTE: This is a guest post by Leopoldo Martínez D., a researcher and consultant on social media corporate intelligence and lecturer at UCV and IESA (Venezuela), and it was originally published on his blog (in Spanish).

 

  1. Introduction

As I stated in the first part of this post, I will show how online reputation evaluation was used in a real situation related to the tourism industry.

  1. The unexpected event: A shooting at a music festival in Playa del Carmen, Riviera Maya

January 6-15, 2017, a music festival was scheduled to be held in Playa del Carmen, as well as a series of events related to both music and the tourism industry. On January 15, a shooting occurred in a well-known bar where people were celebrating the end of the festival.

When the shooting happened, messages quickly spread through social networks to give information and comment on the context in which the incident and how it happened. Some conversations revealed an interesting fact: The shooting was not an isolated event but stemmed from the “situation of crime that the Riviera Maya went through in 2011″.

Could this affect the Riviera Maya’s reputation as a tourist destination? Could “several years in a situation of crime” have already influenced the tourism industry’s image? These are some of the questions that the public and private actors that provide services and products in this tourist area might have been asking themselves.

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Active social listening to protect Online Reputation (Part 1)

EDITOR’S NOTE: This is a guest post by Leopoldo Martínez D., a researcher, consultant on social media corporate intelligence and lecturer at UCV and IESA (Venezuela), and it was originally published on his blog (in Spanish).

 

1. Introduction

In this previous post, I suggested that conversations taking place in virtual communities fostered by a digital marketing plan generate feedback that is useful for assessing and monitoring a digital’s marketing strategy’s performance.

This feedback could generate a huge amount of valuable data (Big Data) which enables the creation of a knowledge base for the topic being talked about, who is participating, who is having the greatest impact on brand image, products, people, or organizations.

This knowledge base can also be fed by discussions arising from unexpected events which are not part of the communication plan but deal with the virtual community’s topics of interest.

To specifically assess the conversation’s impact, it is necessary to pay attention (beyond listening) to what is being said through metrics (qualitative and quantitative) that reflect the online community’s perception on brands, products, people, or organizations. After all, this perception is a way to measure an online reputation.

With this need in mind, the purpose of this post is to show how to use the active listening of conversations in social networks to evaluate your online reputation.
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#ILovePolitics: Political discourse analysis in social media

We continue with the #ILovePolitics series of tutorials! We will show how to use MeaningCloud for extracting interesting insights to build your own Political Intel Reports and, at the same price, turning you into a Data Scientist giant in the field of Social Media Analytics.

political issues

Political issues

Politics and Social Media Analytics

Our research objective is to study and compare the discourse of different politicians during the electoral campaign, using their messages in Twitter. We are going to compare tweets by the four most popular (mentioned) politicians in our previous tutorial: Barack Obama (@barackobama), Hillary Clinton (@HillaryClinton), Donald Trump (@realDonaldTrump) and Jeb Bush (@JebBush).

  • What are their key messages?
  • What do they focus on?
  • Are really there different ways of doing politics?

Before we start, three remarks: 1) we will focus on U.S. Politics, in English language, but the same analysis can be adapted for your own country or language as long as it is supported in MeaningCloud, 2) this is a technical tutorial: we will develop some coding, but in general, everyone can understand the purpose of this tutorial, and 3) although this tutorial will use PHP, any non-rookie programmer can translate the programs to any language.

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Voice of the Customer Analysis and Benefits

 

What is the Voice of the Customer?

Social MediaHave you ever wondered why certain products or services undergo radical changes or even disappear from the market (and sometimes return with another trade name)? Does it depend only on the volume of sales or other factors come into play? To answer these questions, we should introduce the concept of “Voice of the Customer Analysis” and find out what it means. This term refers to all those practices which enable to understand what a (real or potential) customer thinks about a product or service. But it is not limited to a simple reading of comments or opinions written upon request -e.g. an online survey-, the issue is much more complex.

In recent years, the types of channels through which customers and users express their opinions, complaints, suggestions or congratulations (yes, these are also important, then we will see why) have multiplied exponentially. Only a decade ago, the channels that permitted the interaction with the business world were significantly fewer, among them we may recall the telephone or pre-compiled polls often sent by traditional mail. In addition, most of the exchanges between customer and company responded to a specific need of the second; in other words, they were requested.

 

How has it changed?

Today, the picture has radically changed.Voice of the Customer Analysis The communication channels are numerous and also allow to interact in different ways through various media (images, audio, video, etc.). And what matters most to us is that this interaction

  • is constant: 24 hours a day, 365 days a year;
  • most of the times is multilingual;
  • does not always follow predefined patterns (many times, it doesn’t even comply with the most basic spelling rules);
  • is unstructured: it is not stored in a traditional database nor organized according to predefined criteria.

There is no doubt that, from a corporate perspective, this enormous amount of information can be highly beneficial!
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Could Antidepressants Be the Cause of Birth Defects?

We agree that it is not typical at all for an Information Technology company to talk about antidepressants and pregnancy in its own blog. But here at MeaningCloud we have realized that health issues have a great impact on social networks, and the companies from that industry, including pharmas, should try to understand the conversation which arises around them. How? Through text analysis technology, as discussed below.

Looking at the data collected by our prototype for monitoring health issues in social media, we were surprised by the sudden increase in mentions of the term ‘pregnancy’ on July 10. In order to understand the reason of this fact, we analyzed the tweets related to pregnancy and childbearing. It turned out that the same day a piece of news on a study issued by the British Medical Journal about the harmful effects that antidepressants can have on the fetus had been published.
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Exploring Social Media for Healthcare Data

People enjoy sharing information through social media, including healthcare data. Yeah, it is true! And it constitutes the starting point of the research work titled ‘Exploring Spanish health social media for detecting drug effects’, which aims at following social media conversations to identify how people talk about their relation with drug consumption. This allows identifying possible adverse effects previously unknown related to these drugs. Although there is a protocol to communicate to the authorities the identification of a drug adverse effect, only a 5 – 20% of them are reported. Besides, conversations around drugs, symptoms, conditions and diseases can be analyzed to learn more about them. For example, it is possible to see how people search for specific drugs using social media, while others sell them, perhaps illegally. Many others talk about mixing alcohol with drugs or other illegal substances. Of course, one cannot believe everything that appears on the Internet —this is another issue—, but it can highlight some hypothesis for further research.

drugs

Some researchers from the Advanced Databases Group at Carlos III University of Madrid have carried out the mentioned study, designing hybrid models to capture the needed knowledge to identify adverse effects. The Natural Language Processing platform which supports the implementation of the analysis process based on such models is MeaningCloud. The customization capabilities provided by the platform have been decisive to include specific vocabulary and medical domain knowledge. As we know, the names of drugs and symptoms might be complex and, in some cases, difficult to write properly. The algorithm’s results are promising, with a 10% increase in recall when compared to other known algorithms. You can find further details in the scientific paper published by the BMC Medical Informatics and Decision Making Journal.

These developments have been part of the TrendMiner project, and are now available in the prototype website TrendMiner Health Analytics Dashboard, which shows people’s comments about antidepressants gathered from social media. The console displays the mentions of antidepressants and related symptoms and, by clicking on any of them, their evolution over time. Moreover, the source texts analyzed to compute those mentions are shown at the bottom, with labels highlighting the names of drugs, symptoms or diseases, and any relations among them. Such relations might say if a drug is indicated for a symptom or if a disease is an adverse effect of the mentioned drug. The prototype also allows searching by the ATC code (Anatomical Therapeutic Chemical Classification System) and the corresponding level according to this classification scheme. So, if you mark the ‘By Active Substance’ selector, you are searching any drug containing the active substance of the product you inserted in the search box. Furthermore, the predictive search functionality makes easier to find the right expression for a drug or disease.

Health and pharma companies can exploit their unstructured information

There are new kinds of data that are specific to the healthcare and pharmaceutical industries (such as electronic health records) as well as data science tools that allow us to extract valuable knowledge from that data.

 

With MeaningCloud, it is possible to identify the costs of medical treatments, their efficiency (cost, benefits, and risks), references to drugs, side effects, or long-term results. That is why our text analytics solution for the healthcare and pharma domains has so much potential.


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#TuitometroMadrid: a demonstration of MeaningCloud’s capabilities

Using MeaningCloud’s APIs we have developed in a few days a social monitoring tool for a highly topical theme: the local and regional elections in Spain.

Due to the great expectations raised by the upcoming elections of May 24th, several initiatives have appeared that try to analyze the conversation in social media about the different policy options.
We would like to show you one of them, which won’t be given the medal for arriving first, but will definitely win one for being the fastest (we will explain later this apparent contradiction).
At MeaningCloud we have developed #TuitometroMadrid (in Spanish), an application that enables to analyze thoroughly and in real time the conversation on Twitter about the political parties and candidates shortlisted for the Community of Madrid and Madrid’s City Council.

TuitometroMadrid Home

#TuitometroMadrid allows to monitor the buzz, the opinions, and the relevant terms and hashtags around each political option and to compare them aggregately.

TuitometroMadrid Sentiment

Why do we say that it is the fastest tool? Because, besides the fact that it provides the information virtually in real time (and not as post hoc reports), it’s development has been the quickest: by using MeaningCloud’s APIs, an engineer implemented all the semantic analysis of social content in less than one day.
Apart from its usefulness as an informative tool, #TuitometroMadrid is a demonstration that semantic analysis technologies serve to solve real problems in a simple and affordable way.

Would you like to embed semantic analysis into your applications in the easiest, most customizable and affordable way? Use MeaningCloud for free.

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Emergency Management through Real-Time Analysis of Social Media

Serving citizens without paying attention to social media?

App Llamada Emergencias

The traditional access channels to the public emergency services (typically the phone number 112 in Europe) should be extended to the real-time analysis of social media (web 2.0 channels). This observation is the starting point of one of the lines which the Telefónica Group (a reference global provider of integrated systems for emergency management) has been working in, with a view to its integration in its SENECA platform.

Social dashboard for emergency management

At Daedalus (now MeaningCloud) we have been working for Telefónica in the development of a social dashboard that analyzes and organizes the information shared in social networks (Twitter, initially) before, during and after an incident of interest to emergency care services. From the functional point of view, this entails:

  • Collecting the interactions (tweets) related to incidents in a given geographical area
  • Classifying them according to the type of incident (gatherings, accidents, natural disasters…)
  • Identifying the phase in the life cycle of the incident (alert or pre-incident, incident or post-incident)

Benefits for organizations that manage emergencies

Love Parade Duisburg

Love Parade Duisburg

Anticipate incidents

Anticipation of events which, due to their unpredictability or unknown magnitude, should be object of further attention by the emergency services. Within this scenario are the events involving gatherings of people which are called, spread or simply commented through social networks (attendance to leisure or sport events, demonstrations, etc.). Predicting the dimensions and scope of these events is fundamental for planning the operations of different authorities. We recall in this respect the case of the disorders resulting from a birthday party called on Facebook in the Dutch town of Haren in 2012 or the tragedy of the Love Parade in Duisburg.

Flood in Elizondo, Navarre, 2014

Flood in Elizondo, Navarre, 2014

Enrich the available information

Social networks enable the instant sharing of images and videos that are often sources of information of the utmost importance to know the conditions of an emergency scenario before the arrival of the assistance services. User-generated contents can be incorporated to an incident’s record in real time, in order to help clarify its magnitude, the exact location or an unknown perspective of the event.

 

 

Text Analytics technology

Logo MeaningCloud

For the analysis of social content, the text analytics semantic technology (text mining) of MeaningCloud is employed. Its cloud services are used to:

  • Identify the language of the message
  • Classify the message according to a taxonomy (ontology) developed for this scenario (accidents of various kinds, assaults, natural disasters, gatherings, etc.)
  • Extract the mentioned entities (names of people, organizations, places) and the message’s relevant concepts
  • Identify the author or transmitter of each tweet.
  • Extract the geographic location of the transmitter and the incident
  • Extract the time of the message and the incident
  • Classify the impact of the message
  • Extract audiovisual (pictures and videos) and reference (links to web pages, attached documents…) material mentioned in the tweet for documenting the incident
  • Group automatically the messages relating to a same incident within an open record
  • Extract tag clouds related to incidents

Twalert Console

Twalert ConsoleA multidimensional social perspective

Text analytics components are integrated into a web application that constitutes a complete social dashboard offering three perspectives:

  • Geographical perspective, with maps showing the location of the messages’ transmitters, with the possibility of zooming on specific areas.
  • Temporal perspective: a timeline with the evolution of the impact of an incident on social networks, incorporating sentiment analysis.
  • Record perspective: gathering all the information about an incident.

Twitter Accidente Trafico

LT-Accelerate

Telefónica and Daedalus (now MeaningCloud) at LT-Accelerate

Telefónica and Daedalus (now MeaningCloud) will jointly present these solutions at the LT-Accelerate conference (organized by LT-Innovate and Seth Grimes), which will be held in Brussels, on December 4 and 5, 2014. We invite you to join us and visit our stand as sponsor of this event. We will tell you how we use language processing technologies for the benefit of our customers in this and other industries.

 

Register at LT-Accelerate. It is the ideal forum in Europe for the users and customers (current or potential) of text analysis technologies.

Telefonica_logo

 

 

 

 

 

Jose C. Gonzalez (@jc_gonzalez)

[Translation from Spanish by Luca de Filippis]