Quality Management in Social Business Intelligence Projects
María José Aramburu
, Rafael Berlanga
and Indira Lanza-Cruz
Department de’Enginyeria i Ciència dels Computadors, Universitat Jaume I, E-12071 Castelló de la Plana, Spain
Department de Llenguatges i Sistemes Informàtics, Universitat Jaume I, E-12071 Castelló de la Plana, Spain
Keywords: Quality Management, Social Data, Business Intelligence.
Abstract: Social networks have become a new source of useful information for companies. Increasing the value of social
data requires, first, assessing and improving the quality of the relevant data and, subsequently, developing
practical solutions that apply them in business intelligence tasks. This paper focuses on the Twitter social
network and the processing of social data for business intelligence projects. With this purpose, the paper starts
by defining the special requirements of the analysis cubes of a Social Business Intelligence (SoBI) project
and by reviewing previous work to demonstrate the lack of valid approaches to this problem. Afterwards, we
present a new data processing method for SoBI projects whose main contribution is a phase of data exploration
and profiling that serves to build a quality data collection with respect to the analysis objectives of the project.
Social networks have become a new source of useful
information for companies, helping them, among
others, to know the opinions of their customers, to
analyse the trends of the market, and to discover new
business opportunities (Ruhi, 2014). Although social
media data are highly heterogeneous and difficult to
manage, they can produce meaningful information for
decision-makers. The research here presented focuses
on how to build quality social media data collections
for Social Business Intelligence (SoBI) projects.
SoBI is defined in (Gallinucci et al., 2015) as the
discipline that aims at combining corporate with
social media data to let decision-makers analyse and
improve their business needs based in the trends and
moods perceived from de environment. Until now,
companies have used social networks mainly for
marketing purposes. SoBI tools are often applied by
marketing departments to monitor the results of their
activities by means of a group of social media metrics
(e.g. number of likes, followers, or replies) (Keegan
& Rowley, 2017) and methods (Lee, 2018). However,
social media metrics are rarely combined with other
business measures to calculate key performance
indicators of different purpose (Ruhi, 2014). The
integration of social media metrics with corporate
data to produce new strategic indicators are some of
the SoBI related applications that can bring new
opportunities to companies (Agostino et al., 2018).
In current SoBI projects, filtering Twitter by
means of a set of keywords (i.e., topics and hashtags)
generates a flow of potentially relevant tweets. By
processing them, a group of measures and attributes
is extracted which are used to calculate metrics and
indicators. However, most times, in the large volume
of data retrieved, there are many tweets apparently
related to the subject of analysis but that, because of
their origin, intention or specific contents, are not
useful. Therefore, we consider that before processing
the tweets, some filtering tasks are necessary.
However, previous frameworks for social media
analytics have not given the required importance to
data quality in their data preparation phases (Stieglitz
et al., 2018).
In this paper, we highlight the importance of
building a quality data collection in the data
preparation phase of SoBI projects. After defining in
Section 2 the special requirements of the analysis
cubes of a SoBI project and reviewing, in Section 3,
previous work to demonstrate the lack of valid
approaches to this problem, we present, in Section 4,
a new data processing method for SoBI projects. Its
main contribution is a new phase of exploration and
profiling of the retrieved data that serves to build a
quality data collection with respect to the analysis
objectives of the project.
Aramburu, M., Berlanga, R. and Lanza-Cruz, I.
Quality Management in Social Business Intelligence Projects.
DOI: 10.5220/0010495703200327
In Proceedings of the 23rd International Conference on Enterprise Information Systems (ICEIS 2021) - Volume 1, pages 320-327
ISBN: 978-989-758-509-8
2021 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
Traditional Business Intelligence (BI) projects apply
several types of tools to exploit the facts of a subject
of analysis, like for example, the sales of a company.
Usually, these facts are represented into the analysis
cubes of a multidimensional data model. A subject of
analysis is a business context dependent description
that clearly determines the analysis objectives of the
project, and consequently, the measures and
dimensions of its analysis cubes.
In the case of a SoBI project, the facts that serve
to feed the analysis cubes come from a collection of
user-generated contents (e.g. tweets). These contents
are external to the company and consists of
unstructured data with a high level of heterogeneity,
which makes much more difficult to obtain valid
facts. Here, the main issue is that by simply
translating the subject of analysis into a group of
keywords, the retrieved tweets will include many
posts generated with very different purposes and out
of the scope of the SoBI project. Consequently, many
of the retrieved posts do not add any value to the
analysis tasks and may even be counterproductive,
due to the misinformation and the noise that they can
introduce in the intended analysis. For example, in
our experiments, when retrieving tweets with
opinions from the users of a Ford car model, it is
impossible to avoid the retrieval of many memes
about the actor Harrison Ford and the words “fiesta”,
“escort” or “focus”, which also refers to different
Ford car models.
2.1 Defining Analysis Cubes
The subject of analysis of a SoBI project must be
contextualised in terms of the organization's strategic
objectives (Ruhi, 2014) (Lee, 2018). In our approach,
this task consists in defining the analysis objectives
of the project by means of an analysis cube, that is, of
a group of measures which will be analysed from
different points of view and levels of detail. Among
these measures, there can be metrics extracted by
processing tweet contents together with other coming
from tweets metadata (e.g. number of followers,
replies, likes, …). The relevant data will depend on
the analysis objectives to be achieved. These are some
If the objective is brand awareness, metrics
such as share of voice, are an indicator of the
visibility of the brand with respect to its
To gain engagement from customers or
prospects, audience metrics such as number of
comments, shares and trackbacks serve to
define good indicators.
To improve customer service, monitor the
comments and negative sentiment of customers
to generate the right responses and solutions, as
well as indicators such as resolution time and
satisfaction scores after giving support.
Additionally, the dimensions of the analysis
cubes should represent all the attributes, categories,
and hierarchies that the analysis objectives of the
project require. For example, when the managers of a
car brand want to analyse the sentiments and opinions
about their models, they can apply several measures
and points of view. By processing tweet contents, it
can be possible to estimate measures such as the
general polarity of a tweet from opinions about both
the brand or the car model, as well as, the specific
aspects assessed by the user (i.e. engine, ecology,
design, …). The temporal dimension can be applied
to follow up the evolution of these measures, and the
spatial dimension would allow studies by considering
the geographical distribution of the sources of
opinion. However, to discover new valuable insights,
it would be interesting to have additional dimensions
for the categories of car models of the brand as well
as for the different car aspects. Furthermore, to have
the measures mapped against a dimension of types of
users would allow the execution of analysis
operations adapted to the different actors of this
application (e.g. journalists, professional drivers,
general drivers, …). Modelling all these elements
requires understanding the brand business model and
its strategic objectives.
When building an analysis cube, a main issue is
how to obtain a social media data collection from
where to extract the measures of the analysis cubes
along with all the dimensional attributes. To produce
reliable insights, the analysis cubes must be complete
and uniformly filled, with no gaps nor biased
dimensions. For this reason, it will be necessary to
retrieve enough tweets by means of a full range of
possible keywords and hashtags but, at the same time,
to avoid overloading the collection with redundant
and non-relevant tweets. Therefore, it is necessary to
execute filtering operations on the collection.
Determining the feasibility of the project and the
range of retrieval and cleansing operations needed to
build a quality social media data collection is a
complex task that current methodologies do not give
enough importance to.
Quality Management in Social Business Intelligence Projects
Defining a methodology to build quality collections
of social media data involves several things. By one
hand, it is important including in the methodology
some data cleaning operations. On the other hand, it
is necessary to determine the best quality metrics to
be applied by cleaning operations as well as to assess
the quality of the overall collection. In this section,
we review previous methodologies from the point of
view of quality management, and then, we summarise
the different approaches to measuring credibility, the
most important quality attribute for social media.
3.1 Previous Methodologies
Modern methodologies for processing Big Data
consider that quality management should take part of
all the phases of the pipeline (Taleb et al., 2018)
(Pääkkönen & Jokitulppo, 2017). Considering the
data acquisition phase, the Big Data quality
framework presented by (Taleb et al., 2015)
highlights the need for cleansing, integration, filtering
and normalization operations to improve the quality
of the collection and, in this way, to save on costs and
perform accurate data analysis.
The uniform data management approach of
(Goonetilleke et al., 2014) reviews three main groups
of research challenges to address when building a
Twitter data analytics platform. For data collection,
the main issue is the specification of the best set of
retrieval keywords and hashtags; for data pre-
processing, they demand for specific data processing
and extraction strategies for Twitter data; and finally,
for data management, they explain that quality
management is a major issue that requires declarative
languages to query social networks. However, as
noted by (Stieglitz et al., 2018), in the papers that
already document the data tracking and preparation
steps of their social media projects, these steps are
often dealt with superficiality, never with as much
extension as data analysis tasks. The authors conclude
that data discovery, collection and preparation phases
for social media analytics require more research.
In the quality management architecture for social
media data of (Pääkkönen, P. & Jokitulppo, J., 2017),
the data acquisition, data processing & analysis, and
decision-making phases can include functionalities
for quality control and monitoring. In this approach,
data quality management consists in assigning values
to quality attributes which can be applied at
extraction, processing, and analysis time from the
point of view of the data source, the data and the user
respectively. The quality, organizational and
decision-making policies of the organization define
the criteria to filter the quality data. Although, the
proposed architecture can represent all these data
quality elements, the authors do not propose a
methodology for defining and applying them.
The methodology for SoBI of (Francia et al.,
2016) recognizes that crawling design can be one of
the most complex and time-consuming activities and
aims at retrieving in-topic clips by filtering off-topic
clips. They also explain that filtering off-topic clips at
crawling time could be difficult due to the limitations
of the crawling languages and propose to filter them
at a later stage by using the search features of a
documents database. The authors note that manually
labelling a sample of the retrieved clips enables the
team to trigger a new iteration where the crawling
queries are redefined to cut off-topic clips out more
effectively. However, the proposed methodology
does not include any cleaning operations in its
functional design nor determines whether to process
them before or after retrieving the social data. This
work does not consider the quality of data as a main
objective, and it does not explain how to obtain a
good quality collection.
A second methodology for SoBI, proposed by
(Abu Salih, 2015), consists of five stages that process
social media data and integrate them in the data
warehouse files. They propose to execute cleaning
operations to remove dirty data at the data acquisition
stage prior to data storage. Afterwards, during data
analysis, the collected data is processed to infer a
value of trust for the relevant data. In the last stage of
this methodology, a data structuring process serves to
integrate traditional and social media data in order to
produce new insights. In this way, the exploited social
media data has a minimum level of trust with respect
to its domain, although, the analysis objectives of the
SoBI project are not considered as additional criteria
to validate the source data.
3.2 Quality Metrics for Social Media
Measuring social media data quality can be
performed using different metrics and techniques.
The literature review clearly reveals that credibility is
the most important quality attribute for social media,
and many different approaches have been proposed to
measure it. It is important to clarify that for these
authors, credibility is a broad concept that intersects
with other semantically related quality attributes such
as trust, reliability, believability, veracity, relevance,
validity and, in some cases, even understandability
and reputation.
ICEIS 2021 - 23rd International Conference on Enterprise Information Systems
For measuring the credibility of social media data,
statistical or machine learning techniques are usually
adopted. Among the many metrics used to feed these
algorithms, there are those obtained by processing
tweets contents, mainly looking for textual properties,
writing styles, linguistic expressions, sentiments, and
additional elements like URL´s or pictures. Social
parameters extracted from tweets metadata about the
post and its poster are a second source of metrics.
Finally, there are a last group of metrics with
information about the behaviour and actions of the
users in the social network. Table 1 shows metrics
applied in the literature to measure credibility (Sikdar
et al., 2013) (Gupta et al., 2014) (Viviani & Pasi,
2016) (Păvăloaia et al., 2020) and which, in many
cases, could also be applied to assess other quality
attributes. The wide range of metrics applied shows
that, in each approach, credibility can be understood
in a different way, and that it is up to the user to
choose the best metrics taking into account the
context of each project, its domain and the
technologies applied.
In social media, a quality attribute of utmost
importance is reputation which can be also defined as
the authority of the poster. Previous work frequently
considers simple measures such as the number of
followers to calculate indicators of good reputation,
i.e. when a user is in many Twitter lists and has many
followers is because the contents that generates
satisfy many users. This approach ignores that user’s
interest can be diverse and evolve and change over
time. However, recently, more realistic approaches
have proposed to consider this quality attribute as a
time and domain-dependent parameter (Abu-Salih et
al., 2020).
Finally, in (Pasi et al., 2019), the adequacy and the
potentialities to describe the issue of the assessment
of the credibility of user-generated content in social
media as a multi-criteria decision-making problem
have been discussed. Their approach to determining
the credibility of online reviews considers features
connected to the contents, the information sources
and the relationships established in social media
platforms. These features are evaluated by the users
in terms of their impact on veracity. By considering
different aggregation schema for the partial
performance scores and their impact, the authors
calculate an overall score of veracity. With respect to
data-driven approaches based on Machine Learning
techniques, their approach makes the user more aware
of the choices that led to the proposed decision and
can make the considered problem less data-
In this section, we have reviewed relevant
methodologies and techniques to build collections of
social media data for SoBI from the point of view of
quality management. The conclusion is that while
most approaches to social media analysis for decision
support apply different quality criteria during data
collection and preparation, it is not clear at this point
how to define a general-purpose quality conceptual
model for social media data. Previous work has
provided us with many different metrics with multiple
purposes that depend primarily on the application.
The experience demonstrates that, whatever
technology applied to decide the quality of data, a
good combination of different types of metrics use to
be part of the solution. However, there is no
systematic methodology for defining and applying
these metrics in order to build a reliable collection of
social data. In this case, the main question is how to
find the best metrics that can be applied to both
prepare the collection and measure its quality with
respect to the analysis objectives of a SoBI project,
i.e. the construction of an analysis cube with a set of
social measures that can be analysed from different
points of view and levels of detail.
Table 1: Sample of metrics to measure credibility in social media data found in the literature.
Tweet contents Post and poster metadata User behaviour
# Chars/words
# Punctuation symbols
# Pronouns
# Swear words
# Uppercases
# Emoticons
# Hashtags
# Misspelled words
# Sentences
Average length of sentences
# Product mentions
# Product features mentioned
# Opinion sentences
Account age
Listed count
Status count
Favourites count
# Friends
# Followers
# Followings
Ratio of followers to friends
Mean text length in tweets
Mean hashtags in tweets
Mean # URLs/ mentions in tweets
Verified user
User image in user profile
Tweet geographical coordinates
# Retweets
# Tweets
# Tweets favorited
# Mentions
# Tweets are a reply/retweet
Mean time between tweets
# Likes received
# Directed tweets
# Users that propagate the user
# Users the user propagates from
# Tweets propagated by other users
# Users that converse with the user
Mean number of conversations
Average length of chain-like behaviour
Quality Management in Social Business Intelligence Projects
Evaluating the quality of social media data for a SoBI
project requires the definition of the best quality
indicators for the source data (Immonen et al., 2015).
Tweets present many different aspects that would
serve to filter them; being Tweet contents and users’
attributes and actions, the main sources of quality
metrics. However, the selection of the best quality
metrics for a SoBI project is a complex task that
requires a deep understanding of the business context
and objectives of analysis, as well as the social media
data to be managed (Berlanga et al., 2019).
In data intensive applications, quality conditions
should serve as criteria to program the cleaning
operations and to measure the quality of the overall
collection. As explained in (Sadiq & Indulska, 2017),
traditional methods for managing data quality follow
a top-down approach: the analysis of user
requirements produces some quality rules that serve
to govern data, to assess data quality, and to execute
cleaning operations. This approach is suitable for
managing the quality of data generated internally by
an organization. However, when the organization
does not control the external processes that generate
the available data, as in the case of social media data,
quality assessment requires prior knowledge about
the data. In these cases, the data quality management
follows a bottom-up approach that starts with the
execution of some exploratory analysis and data
profiling tasks. These tasks help to find data quality
rules and requirements that will drive the data
collection process. In order to execute the preliminary
exploration of the available data, interactive,
statistical and data mining techniques can be applied
over collections of data. Some of the data mining
techniques that can help in these tasks are clustering,
classification, data modelling and data summarization
(Stieglitz et al., 2018).
Following this approach, in this section we
present a new method for Social Media data
processing for SoBI applications whose main
contribution is a first phase of exploration and
profiling of the retrieved data that serves to build a
quality data collection with respect to the analysis
objectives of the project.
4.1 A Data Processing Methodology
The analysis objectives of a SoBI project are to
analyse from various perspectives (i.e. dimensions
and categories of analysis) the social measures drawn
from a complete collection of relevant tweets. Often
these will be simple measures such as the number of
likes or followers, especially if the aim is to analyse
the success of a marketing campaign. For example, if
the objective is brand awareness, social measures
such as voice share are an indicator of the brand's
visibility in relation to its competitors that can be
calculated by counting hashtags and mentions.
However, there are other more difficult measures to
analyse, such as when analysing feelings about
different aspects of a product or service (e.g. a holiday
package or a car).
To deal with the completeness of the collection
and the relevance of the tweets in it, in this paper we
propose a new data processing methodology that
consists of three main phases: Collection
Construction, Data Preparation and Data Exploitation
(see Figure 1). The first phase is the construction of a
collection of tweets through an exploratory process
executed by the user and directed by the quality of the
recovered data. When a quality data collection is
ready, in the data preparation phase, the facts of the
analytical cubes are extracted from the posts and then
exploited in the last phase of the process.
As Figure 1 illustrates, during the Collection
Construction phase, the user executes some data
exploratory and profiling tasks to assess and improve
data coverage and data quality until obtaining a
quality collection that meets the project's analysis
objectives. More specifically, this phase consists of
two complementary and iterative tasks:
a) Evaluating the subject coverage of the
collection with respect to its topics and users.
Analysing the vocabulary of the collection will
help to know when to redefine the keywords
applied to program the Twitter API to obtain
either a more complete or precise collection.
Furthermore, profiling the range of users that
post the retrieved tweets along with their
metadata is important in determining the
measures and dimension attributes available to
be part of the analysis cubes, as for example,
the users’ demographic data present in their
b) Analysing and improving the quality of the
collection by filtering the posts of low quality
or out of the scope. Finding the best quality
metrics that help to clean a collection requires
exploring and profiling it in order to discover
its main characteristics and the sources of
noise. Then, cleaning operation can apply
different types of quality metrics extracted by
processing the tweets contents and metadata as
well as the user descriptions. For example,
ICEIS 2021 - 23rd International Conference on Enterprise Information Systems
Figure 1: The proposed social media data processing method for Social Business Intelligence.
from a collection of user opinions about cars, memes
should be removed because their low added value as
well as the ad posts sent by marketing departments. A
global analysis enhances the detection of long-term
quality problems, such as redundancy, bias and noise,
which are often difficult to detect from local analysis
(i.e., directly over the streamed data). Analysing a
long-term data stream also allows building robust
language models for a given domain, smoothing the
effects of punctual viral events (Lanza-Cruz et al.,
In the Collections Construction and Data
Preparation phases, the processing of tweets to extract
the measures and values that serve both to clean the
collection and to feed the analysis cubes, can be made
in different ways. Some values are directly available
in the tweets metadata, such as post-date and number
of followers of the user. Other values can be
calculated with a simple processing like counting
tweets over a period. Evaluating the grammatical
richness of a post is executed by a process that
calculates some textual measures (Gupta et al., 2014).
However, more sophisticated Natural Language
Processing techniques are required when analysing
the vocabulary of the whole collection (Berlanga et
al., 2019) or extracting the facets and polarity of
opinion expressions (García-Moya et al., 2011).
Other intelligent techniques like Entity Resolution
and Ontology Mappings, mainly applied for the
Semantic Enrichment and Dimensional Mapping
tasks of Figure 1, have shown useful to extract
dimensional attributes from social media data
(Berlanga et al., 2015) (Pereira et al., 2018).
Furthermore, the tweets in the collection can be
applied to train Machine Learning algorithms that
discover some useful properties of the tweets, as for
example their credibility and intention, and to classify
them into analysis categories. The main objective of
all these tasks is to produce a complete analysis cube
whose measures and dimension attributes can enable
reliable multidimensional studies.
Finally, in the Data Exploitation phase, the
analysis cubes constructed by processing the tweets
collection can be stored into the Corporate Data
Warehouse for future uses. OLAP applications, or
any other Business Intelligence or Data Mining tools,
can be applied to analyse and extract new insights
from these cubes. If necessary, the stored social
media facts can be combined with corporate data to
design new key performance indicators tied to the
strategic business objectives such as ROI and profit
margins. In this way, users can associate social media
actions with sales volume, revenue increases or
decreases and other relevant metrics and reveal new
The focus on integrating social media metrics and
internal business measures is important in our
approach. However, combining qualitative data from
social networks with the quantitative data hosted in
traditional BI systems may seem a difficult task. Note
that as pointed out by (Ruhi, 2014) this will require
the semantic integration of the data elements in
both kinds of external and internal data sources. From
a practical point of view, this integration can
be possible if the dimensional parameters applied
to construct the analysis cubes of social media and
Quality Management in Social Business Intelligence Projects
corporate data are compatible (i.e. mappable).
At this point, it is important to note that the
success of a SoBI project will mainly depend on the
completeness of the analysis cube that can be
constructed. This means that sometimes, it will be
difficult or even impossible to build the collection of
tweets to fill the analysis cube in all its dimensions.
To this end, the data exploration and profiling tasks
of the Collection Construction phase of our method
will serve to assess and improve the coverage of the
data until a quality collection is obtained that will
allow the construction of the analysis cube that meets
all the analysis objectives of the project. For example,
if the objective of the analysis is to study the evolution
of opinions about our products of different types of
market participants (e.g. customers, sellers, users
journalists, ...), it will be necessary to retrieve a
representative sample of opinion tweets about each
product in our catalogue, for all the dates of the
analysis period, and for each type of user profile.
In addition, when the analysis cube must be
complete, the availability of metadata becomes an
important issue. For example, some analysis tasks,
such as segmentation of market opinions by gender,
age, location, or profession, require extraction of
metadata from tweets and user descriptions.
However, Twitter users do not always provide these
parameters and some tweets will not be valid because
they lack some key dimensional values. This issue
may mean that the SoBI project is not feasible and
that its analysis objectives should be modified by
redefining or eliminating some attributes or
dimensions of the analysis cube. For example, in most
cases, the analysis of the opinions of consumers must
be executed at country level due to the lack of precise
metadata with the geo-location of the users.
Up to our knowledge, this is the first general approach
to the construction of social media data collections
oriented towards corporate BI tasks. With respect to
previous work, the main contribution of this
methodology is that it considers collection
construction as an iterative exploration process in
which the user analyses the current collection from
the point of view of the analysis objectives and
discovers clues about how to improve it. This data
exploration & profiling task has not received enough
attention in previous methodologies and projects and,
however, in our experiments we have validated that it
helps users to identify the retrieval conditions and
cleaning operations that the construction of each
specific collection requires, as well as, to assess the
feasibility of the analysis objectives of the SoBI
project with respect to the availability of reliable
social media data (Lanza-Cruz et al., 2018) (Berlanga
et al., 2019) (Aramburu et al., 2020).
For future work, an open issue is how to
complement the here presented processing method
with intelligent tools that guide the user in the
selection of the best tweet quality metrics and criteria
for each specific data domain and application. How to
combine them into global quality indicators for a
collection with respect to the analysis objectives of a
SoBI project would be the next step (Berlanga et al.,
2019). New SoBI application scenarios are also
interesting for future research (Aramburu et al.,
This research has been funded by the Spanish
Ministry of Industry and Commerce grant number
TIN2017-88805-R and by the pre-doctoral grant of
the Universitat Jaume I with reference
Abu-Salih, B., Wongthongtham, P., Beheshti,S., Beheshti,
B. (2015). Towards A Methodology for Social Business
Intelligence in the era of Big Social Data incorporating
Trust and Semantic Analysis". In Second International
Conference on Advanced Data and Information
Engineering (DaEng-2015), ed. Bali, Indonesia:
Springer, 2015.
Abu-Salih, B., Chan, K.Y., Al-Kadi, O. et al. Time-aware
domain-based social influence prediction. J Big Data 7,
10 (2020). https://doi.org/10.1186/s40537-020-0283-3.
Agostino, D., Arnaboldi, M., & Azzone, G. (2018). Social
Media Data into Performance Measurement Systems:
Methodologies, Opportunities, and Risks. Azevedo, E.
et al. (Eds.), Handbook of Research on Modernization
and Accountability in Public Sector Management. IGI
Global. 254-275.
Aramburu, M.J.; Berlanga, R.; Lanza, I. (2020) “Social
Media Multidimensional Analysis for Intelligent Health
Surveillance”. Int. J. Environ. Res. Public Health, 17,
2289, https://doi.org/10.3390/ijerph17072289.
Berlanga, R., García-Moya, L., Nebot, V., Aramburu, M. J.,
Sanz, I., & Llidó, D. M. (2015). SLOD-BI: An Open
Data Infrastructure for Enabling Social Business
Intelligence. International Journal of Data Warehousing
and Mining (IJDWM), 11(4), 1-28.
ICEIS 2021 - 23rd International Conference on Enterprise Information Systems
Berlanga, R., Lanza-Cruz I. & Aramburu, M. J. (2019)
"Quality Indicators for Social Business Intelligence,"
2019 Sixth International Conference on Social
Networks Analysis, Management and Security
(SNAMS), Granada, Spain, 2019, pp. 229-236, doi:
Francia M., Gallinucci E., Golfarelli M., Rizzi S. (2016)
Social Business Intelligence in Action. In: Nurcan S.,
Soffer P., Bajec M., Eder J. (eds) Advanced
Information Systems Engineering. CAiSE 2016.
Lecture Notes in Computer Science, vol 9694.
Springer, Cham.
Gallinucci, Golfarelli & Rizzi, (2015). Advanced topic
modeling for social business intelligence. Information
Systems., 53, pp. 87-106.
García-Moya, L., Anaya-Sánchez, H., Berlanga, R.,
Aramburu, M.J. (2011) Probabilistic Ranking of
Product Features from Customer Reviews. In: Vitrià J.,
Sanches J.M., Hernández M. (eds) Pattern Recognition
and Image Analysis. IbPRIA 2011. Lecture Notes in
Computer Science, vol 6669. Springer, Berlin,
Heidelberg. https://doi.org/10.1007/978-3-642-21257-
Goonetilleke, O., Sellis, T., Zhang, X., & Sathe, S. (2014).
Twitter analytics: a big data management perspective.
ACM SIGKDD Explorations Newsletter, 16(1), 11-20.
Gupta, A., Kumaraguru, P., Castillo, C. & Meier, P. (2014).
TweetCred: Real-Time Credibility Assessment of
Content on Twitter. Proceedings of the 6th International
Conference on Social Informatics. 228-243.
Immonen, A., Pääkkönen, P. & Ovaska, E. (2015).
Evaluating the Quality of Social Media Data in Big
Data Architecture. IEEE Access. 3. 1-1.
Keegan, B. & Rowley, J. (2017). Evaluation and decision-
making in social media marketing. Management
Decision. 55. 15-31. 10.1108/MD-10-2015-0450.
Lanza-Cruz, I.; Berlanga, R.; Aramburu, M.J. (2018).
Modeling Analytical Streams for Social Business
Intelligence. Informatics. 5. 33. https://doi.org/10.
3390/ informatics5030033
Lee, I. (2018). Social media analytics for enterprises:
Typology, methods, and processes. Business Horizons.
61.2. 199-210, https://doi.org/10.1016/j.bushor.2017.
Pääkkönen, P. & Jokitulppo, J. (2017). Quality
management architecture for social media data. J Big
Data 4, 6 (2017). https://doi.org/10.1186/s40537-017-
Pasi, G., Viviani,M. Carton,A. (2019). A Multi-Criteria
Decision Making approach based on the Choquet
integral for assessing the credibility of User-Generated
Content. Information Sciences, vol 503. Pages 574-588.
Păvăloaia, V.-D., Anastasiei, I.-D., & Fotache, D. (2020).
Social Media and E-mail Marketing Campaigns:
Symmetry versus Convergence. Symmetry, 12(12),
1940. doi:10.3390/sym12121940.
Pereira,V., Fileto, R., Santos,W., Wittwer,M., Reinhold, O.,
Alt, Rainer (2018). A Semantic BI Process for
Detecting and Analyzing Mentions of Interest for a
Domain in Tweets. WebMedia '18: Proceedings of the
24th Brazilian Symposium on Multimedia and the Web.
197–204, https://doi.org/10.1145/3243082.3243100.
Ruhi, U. (2014). Social Media Analytics as a BI Practice:
Current Landscape & Future Prospects. Journal of
Internet Social Networking & Virtual Communities. 1-
12. 10.5171/2014.920553.
Sadiq, S. & Indulska, M. (2017). Open data: Quality over
quantity. International Journal of Information
Management. 37. 150-154. 10.1016/j.ijinfomgt.2017.
Sikdar, S., Kang, B., ODonovan, J., Höllerer, T. and Adah,
S. (2013). Understanding Information Credibility on
Twitter, International Conference on Social
Computing, Alexandria, VA, pp. 19-24, doi:
Stieglitz, S., Mirbabaie, M., Ross, B., & Neuberger, C.
(2018). Social media analytics - Challenges in topic
discovery, data collection, and data preparation.
International Journal of Information Management. 39.
Taleb,I., Serhani, M. A. & Dssouli, R. (2018), Big Data
Quality: A Survey, IEEE International Congress on Big
Data (BigData Congress), San Francisco, CA, pp. 166-
173, doi: 10.1109/BigDataCongress.2018.00029.
Taleb,I. Dssouli, R. & Serhani, M.A. (2015), Big data pre-
processing: A quality framework. 2015 IEEE
international congress on big data. 191-198.
Viviani, Marco & Pasi, Gabriella. (2016). Quantifier
Guided Aggregation for the Veracity Assessment of
Online Reviews. International Journal of Intelligent
Systems. 32. 10.1002/int.21844.
Quality Management in Social Business Intelligence Projects