Enhancing the Status Message Question Asking

Process on Facebook

Cleyton Souza1, Jonathas Magalhaes1, Evandro Costa2, Joseana Fechine1, andRuan Reis1

1 Laboratory of Artificial Intelligence - LIA,Federal University of Campina Grande - UFCG,

Campina Grande-PB, Brazil.2 Group of Intelligent, Personalized and Social Technologies - TIPS,

Federal University of Alagoas - UFAL,Maceio-AL, Brazil.

Abstract. People have been using Social Networks to search for helpby broadcasting messages that reflect their information needs. However,several factors, usually not considered by the user, influence the outcomeof receiving or not an answer. In this work, we aim to increase the users’chances of finding someone who could help them. For this purpose, wepropose a mobile app called Social Query, which guides the users throughsome steps before they share the problem with their friends. As far aswe know, this is the first work to merge these three aspects of the so-cial search: Question Rephrasing, Expert Search Filtering and ExpertiseFinding. To evaluate our proposal, we ran a questionnaire in which usersconsidered Useful most functions of the app.

Keywords: Social Search, Social Query, Social Network, Status Message Ques-tion Asking

1 Introduction

Currently, Social Networks (SNs) are the most popular service on the Web, sur-passing even E-mail [23]. In this scenario, Facebook stands out as the most popu-lar worldwide SN, with more than one billion users [16]. These SN sites were firstdesigned to allow remote interaction among geographically dispersed people [6].One of the goals of interacting with others is the exchange of knowledge [4].Thus, versions of knowledge exchange emerged from these virtual spaces.

One of the ways that knowledge exchange may occur is by means of StatusMessage Question Asking (SMQA), that is, members of SN sites making useof status messages to express information needs to friends and contacts [15].It is an attempt to transform social relationships in practical knowledge [6].This strategy is particularly useful when we are dealing with high-contextualizedproblems or when we are looking for personalized information [13].

Broadcasting has become a popular way to share questions on SNs. However,it is not the best, especially, in the context of Facebook. First, on Twitter, the

status would appear in all your follower’s timeline, but there is no guaranteethat people able to help will see your status while it stays on the top, and theprobability of being seen decreases as it falls down [2]. However, on Facebook,the feed shown to each user is based on a personalized algorithm [11]; therefore,when someone broadcasts a question, there is no guarantee that it will be seen atany moment by people able to help. To ensure that the question will be at leastvisualized, some works defend that directing questions is more effective thanbroadcasting [14], but knowing who the question should be directed to is notalways easy and, if we choose poorly, the person could just ignore the questionor even give a wrong answer [20]. In addition, the way the question is phrasedcould be decisive to receive an answer.

Teevan et al. [21] found that characteristics of the question itself predictedthe quality, number, and speed of responses. Thus, we notice that making useof SNs to find information is not always simple, because too many factors mustbe taken into account.

In this work, we aim to improve the SMQA process. To help the user, wepropose an app called Social Query, which enhances the probability of receivingan answer, guiding the user through some steps before the disclosure of thequestion on Facebook. We propose a tool to help people looking for help. It isnot only an Expertise Finding System (EFS), but also a tool to assist users tophrase their problems and restrict the social search to a certain demographicgroup. As far as we know, this is the first work to merge these three aspectsof the social search (question rephrasing, expert search filtering and expertisefinding). Through this app, users inform their questions and receive suggestionsto increase the probability of receiving answers. The suggestions range from tipsto rephrase the question to indications about who probably knows the answer(a person or group), so the user could direct the question to specific contacts,ensuring that it will be visualized by someone who could help.

We used a questionnaire to get feedback about our proposal. Through thequestionnaire, people could express their opinions about the functions availablein the Social Query app. The results were excellent; people considered usefulmost of the available functions, but we highlight the acceptance of the ExpertiseFinding engine and the Filtering engine.

The remainder of this paper is organized as follows. Section 2 presents a briefliterature review. Section 3 describes the internal work of our proposal, and Sec-tion 4 presents the Social Query app. In Section 5, we present the questionnaireresults and, finally, Section 6 ends the work with Conclusions and Future Work.

2 Related Work

The literature review is presented in the next sections. In the first part, we discussabout the practice of sharing questions on SNs; next, we list some EFS and, later,we discuss about the main differentials of our work to previous research.

2.1 About Status Message Question Asking

The habit of sharing questions on the web born on Community Questions andAnswering (CQA) sites and was extended to SNs [19]. Asking a question on SNis an explicit action performed by users in order to convert the social relation-ships maintained on the site into actionable information and other social capitaloutcomes [6]. SMQA serve many purposes, including creating social awareness,encouraging the asker to reflect on a current information need, building socialties, and, of course, finding answers [21]. The motivations to answer vary butare mainly Altruism, Feel like an Expert, and Interestingness [13].

Morris et al. [13] presented statistics confirming SMQA as a viable methodto find on-line answers. In their case study, 93.5% of users had their questionsanswered after sharing them and these responses, in 90.1% of the cases, wereprovided within one day. The main motivations pointed by the users who prac-tice the SMQA were (1) their trust on their contacts and (2) the hope of apersonalized answer [13]. These motivations highlight the advantages of pos-ing questions on SNs compared to more generic CQAs sites; in addition, somepatterns, identified by the studies on information seeking, suggest that certaininformation needs, such as those revolving around quotidian occurrences, aremore commonly solved by individuals one already knows (e.g., “Was there a testin English class today? I overslept.”).

Teevan et al. [21] found that characteristics of the question itself predicted thequality, number, and speed of responses: requests that were stated as a question,posed as a single sentence, and explicitly scoped received better responses. Inaddition, Nichols and Kang [14] confirmed that directing questions significantlyimprove the response rate, while the quality of the answer depends on who thequestion will be directed to [18]. In this sense, EFSs play an important role: ifwe identify an expert on the topic of the question and direct it to that expert,the answer would come faster and with higher quality [18]. Next, we will detailsome interesting systems that could be used to this end and are related to ours.

2.2 About Query Routing Systems

The process of directing questions to appropriate helpers is called in literatureas Query Routing (QR) and it is a well explored theme, specially, in the contextof CQAs.

Tomiyasu et al. [22] propose a mobile CQAwith the QR feature. Basically, theusers describe their expertise using keywords and, when someone has a question,keywords are used to describe the subject and the question will be broadcastedto people who have these same keywords in their profiles. This probably is thesimplest way to implement a QR policy. In [12], a similar app is presented, butwith the differential that questions are only directed to the most suitable peopleconnected to the questioner and they have the option to forward the questionto people connected to them. In addition, the system maintains a history ofprevious answered questions to keep forwarding questions that fit with user’scurrent interests. Aardvark [7] was a CQA that belonged to Google in which

people ask and answer questions directed to them according to their expertise.The expertise was informed by the user and his/her friends, and learned throughtheir question and answering history and the information captured through otherchannels (e.g., Gmail, Orkut, Google search history). In addition, users wereallowed to ask questions specifically to their private SN of friends who also werein the system. Unfortunately, the project was closed in 2011 [8].

Regarding QR on the SN context, [5] proposes a topic EFS to Twitter namedCognos, which infers expertise by crowd wisdom captured by user’s List infor-mation. Ghosh’s work focuses on finding global experts on Twitter: the moretimes a user appears in lists related with “computing”, greater the evidence thathe is a computer expert; each appearance is taken as an implicit vote. Lin etal. [9] present SmallBlue, a corporative SN with an EFS. The expertise pro-files are built based on corporative information (e.g., email, personal webpage,chat history). Moreover, when recommending experts, SmallBlue can prioritizepeople close to the requester and there is a filter to restrict the search usingpredefined criteria like department or position. Davitz et al. [3] proposed iLink;a tool for social search and message routing on SN. Davitz’s work included anentity named supernode, which monitors the SN and decides to whom the ques-tions will be routed. In some cases, the supernode is also able to offers answersbased on Frequently Asked Questions (FAQ). However, due the computationalcost to monitor the entire SN, they only evaluate in small forums.

2.3 The differential of our research

The works presented so far are all EFS. Expertise Finding (EF) usually involves aglobal context of candidates, for instance [5]. However, we aim at the detectionof specialists in the set of the questioner’s friends (local context). Thus, the“amount” of expertise necessary to characterize someone as “an expert” changesaccording the set that we are looking into.

Moreover, EF often considers only the expertise about some topic like [22]and [12]; what we are proposing is taking into account several factors to improvethe probability of finding relevant information through the help of friends. In [20],we had proposed a QR system that routed questions to followers on Twitterbased on three criteria: knowledge, trust and activity. However, in this work, weare not proposing a system that just recommends experts.

Here, we propose a tool that also assists the user in the process of sharinghis problem like [7] and [3]. (I) The Social Query app will analyze the questionand suggest modifications; (II) it will suggest restricting the search for help toa certain demographic group (as people with your age or the same professionas you); and (III) it will suggest people based on their bounds, availability andexpertise. As far as we know, this is the first work to merge these three aspectsof the social search (question rephrasing, expert search filtering and expertisefinding). In addition, we propose this to the context of the most popular SNnowadays, instead a private access context, as done in our previous studies.

3 Project & Architecture

In this Section, we explain the internal organization of the Social Query app.Figure 1 presents the architecture of the proposal. The white arrows representinput, the black arrows represent output, and the gray arrows have multiplemeanings (e.g., HTTP request, SQL execution, HTTP response, etc.).

Fig. 1: Social Query app architecture.

As can be seen, this app has four main modules; each module representsan independent function, namely: ‘Expertise Finding Module’, ‘SynchronizationModule’, ‘Rephrase Module’ and ‘Filtering Module’. Together, these last twoform the Question Analyzer, which could be also considered as a module, wherethe input is the question and the output is the set of suggestions to rephrase thequestion (established by the ’Rephrase Module’) and to filter the expert searchto a certain demographic group (established by the ‘Filtering Module’) .

The ‘Synchronization Module’ is responsible for crawling the informationfrom Facebook and updating the local database. This information consists ofdata about the users and their contacts. Figure 2 illustrates the entity ‘User’ inour model.

The attributes of the entity ‘User’ are self-explanatory. In upcoming releases,we are planning to allow users to update their own profiles through the SocialQuery app, but in the current version all information is retrieved from theirFacebook profiles. The ‘Synchronization Module’ retrieves the information whenusers connect the app with their Facebook accounts and every time they use there-sync option in the app’s menu. This information is essential to the ‘ExpertiseFinding Module’. The inputs of this Module are the EF technique, the activeFilters, and the Question. The output is a rank of the questioner’s friends orderedby their fitness with the Question and the Filters.

Fig. 2: Entity ‘User’ and its attributes.

4 Social Query on Facebook: mobile app

The Social Query app was developed for Android. It helps users to use thepotential of their social capital in order to turn social connections into practicalknowledge. In the next sections, we will detail how our app works and the ideasbehind its views.

4.1 First View

The First View of the app is the Login page, shown in Figure 3 (left), whereusers must inform their Facebook credentials (right).

Fig. 3: First view of the app (left) and Facebook’s mobile login dialog (right).

After logon, the user must give us permission to access their Facebook ac-count information and to publish content in their feeds, as presented in Figure 4(left). After that, they are directed to the Main Page, as shown in Figure 4(right).

Fig. 4: Permission’s dialog (left) and main view (right).

The options in the Main page are: Logout (a); go to Settings (c); Synchronizeagain with the Facebook account (d); and Go make a question (b). The Logoutoption directs the user to the Login page again. The Settings option allows usersto choose what EF model to use (currently, there are three available ones) andto define Filters to the EF search. The Synchronize option is an opportunity forusers to update the app information about them (catch more recent informationabout them, their contacts and new connections); it will start the same threadinitiated after the Login. The Go button guides the user to the main functionalityof the app. Next, we detail the Settings option and will later talk about how ourproposal fits the Q&A process.

4.2 Settings

Currently, the Settings option is limited to choosing the EF model and activeFilters to the Expert Search. The EF model is the technique that will be usedto represent the contact’s expertise. The Filters to Expert Search restrict therecommendation to a certain group of contacts. Both are inputs of the ‘ExpertiseFinding Module’. Next, we will discuss about the EFModels and Filters availablein Social Query app.

Expertise Finding Models In the current version of the app, there are threeEF models available to the users, namely:

– Voting Model: Proposed by Macdonald and Ounis [10], it considers the taskof ranking experts as a voting problem. The profile of each expert candidateis associated to a set of documents that represent their expertise. The requestfor an expert is assumed as a query in a search engine that retrieves someof these documents. Each retrieved document is associated to one or manyusers and counts as an implicit vote for them. The ranking of experts isbased on the total of votes of each candidate. Several strategies could beused for retrieving the documents, associating the document to the users orweighting the votes.

– Vector Space Model: A classical approach from Information Retrieval(IR), proposed originally in [17]. The idea behind the model is to representcontent in multidimensional vectors. In our context, the vector representsthe content associated with each user, the coordinates represent the words,and the coordinate values are calculated using TD-IDF. The expertise scoreis the similarity between the expertise profile and the question vector usingcosine similarity.

– PageRank: A classical algorithm that measures the importance of a nodecounting the number and quality of nodes pointing to it [1]. If we considerthat the scenario where “a user X , author of question Q, receives an answerA, from user Y ” represents a graph like X → Q → A → Y , that couldbe simplified to X → Y . One of the goals of PageRank is to estimate theprobability of randomly getting into a node; the higher this probability, thegreater the odds of the node being a good recommendation.

Filters The Filters are used to restrict the social search to a certain socialgroup. Currently, there are five Filters implemented. Restrict by: age, gender,profession, formation and location. In the current version of the app, each filterrestricts the expert search to people with the same characteristics of the user.For instance, if the user is a man and he checks the “Filter by gender”, onlymen will be recommended; if he lives in Paris and he also checks the “Filter bylocation”, only men who live or lived in Paris will be recommended as well.

However, for the upcoming releases of the app, we are planning improvementsto the filter engine. One of the improvements will allow users to choose the filtervalue (e.g., the hometown that they want to use to restrict the search). Anotherimprovement will be the automatic prediction of the ideal filter value (e.g., findwhat would be the most indicated hometown). In the literature, there is alreadysome research in this direction, e.g., [7]. In addition, we are constantly thinkingabout new Filters.

4.3 Q&A Process

The main goal of the app is to help users before they share their questions onFacebook. For this purpose, we planned to split the Q&A process into threemain screens: one to phrase the question; one to receive suggestions from theQuestion Analyzer; and, one for users to tag people in text of the question.Figure 5 presents these three screens.

The First screen (Part ‘A’ of Figure 5) is where the users phrase their prob-lems. After that, they are directed to the screen where the Question Analyzer’ssuggestions are displayed (Part ‘B’ of Figure 5). Then, they must choose whatsuggestions they will accept. Finally, they see the rank of their friends and checkthose who they want to tag in text of the question (Part ‘C’ of Figure 5).

In Part ‘A’ of Figure 5, the user ‘Ted Mosby’ has a question about places tovisit in Paris. After typing the question, the user will click on the Ask button,being directed to the Tips View. The Part ‘B’ of Figure 5 illustrates some of

Fig. 5: The three screens of the Q&A process. The user enters a question (Part ‘A’);Question Analyzer’s suggestions (Part ‘B’) and; Friends recommendation (Part ‘C’).

the tips that could be given to the user. The decision about which tips will bepresented is determined by our Question Analyzer, based on the characteristicsof the question. The Question Analyzer processes the question and extracts itscharacteristics. Briefly, the Rephrase Module and the Filtering Module look intothe text of the question, searching for specific information (e.g., terms or men-tions to place or people). They associate these characteristics to pre-establishedtips, which were decided based on the literature review and on the interviewsthat we conducted. The chosen tips are displayed to the users, who will havethe option to accept them or not. If they decide to accept any tip, they mustclick the Back button (to edit the question text) or Settings Menu (to turn onsome filters). After that, they can click the Next button to be directed to theRecommendation List View, where they chooses who they want to forward thequestion to. This view is Part ‘C’ of Figure 5. Questioner’s friends are orderedaccording to their utility score, calculated by the EF model chosen on Settings.The users check the people and click the Post button. Then, the Social Queryapp posts on the Facebook user feed the question tagging the friends that theychecked.

5 Results

To validate our tool, we shared a questionnaire with Facebook groups. Thisquestionnaire was answered by 250 volunteers. To know about our volunteers,the first part of questionnaire asked them about their experience with SMQA;the second part request them to value the main function of the Social Queryapp. We decide by the questionnaire instead a case study because it allows usto capture more impressions. The next Subsection summarizes our results.

5.1 Who answered the questionnaire?

We shared the questionnaire in Facebook groups. It was answered by 250 volun-teers. However, only 159 confirmed that had already shared questions through

an SN. In this section, we will briefly describe the experience and habits of thesevolunteers regarding the social query.

Regarding their habits before sharing the question, most volunteers searchfor the answer by themselves before turning to friends for help, only 5% of themadmitted that they go straight to SNs. In addition, most people (84%) often thinkcarefully about how to phrase the problem. It is known that a short period anda well-defined audience are associated with better answers [21]. However, only1/3 thinks about people they know who probably can help. Moreover, 1/3 ofvolunteers also make the “mistake” of being thorough.

Regarding their opinion about how easy to it is find help through SMQA, 130(81%) consider it easy while 29 (19%) consider it hard, but 94% said that theyusually do not need share their problem multiple times to receive an answer.The most common sharing strategy is to publish the question in a group withthe same topic (70%), followed by asking someone directly through chat (62%)and sharing the question through feed (53%). The same strategies, but combinedwith tagging, were not so popular: sharing in group tagging a member obtained10% and sharing in the feed tagging some friend (s), 20%.

This describes some of the volunteers who answered our questionnaire.

5.2 What did they think about the mobile app?

The volunteers evaluated the aspects of the application described in previoussections. Initially, we asked volunteers to value the main functions of our pro-posal. There was a template question like “How useful would be a tool with this[function]?” followed by one of the functions of the Social Query app. The answeroptions were “Don’t know”, “Somewhat Useful”, “Useful” and “Very Useful”.The results are summarized in Table 1.

Table 1: The results of the user questionnaire. The symbol (*) means significance atthe level p < 0.05 and (**) means level p < 0.01 )

Question Don’t Somewhat Useful Very

Know Useful Useful

How useful would be a tool that indicates 4% 12% 47%** 37%friends who probably can answer your question?

How useful would be a tool that suggests 4% 33% 40%* 23%changes to your question in order to increasethe likelihood of getting an answer?

How useful would be a tool that indicates what 2% 12% 43%** 42%**group of your friends is able to help you?

As can be seen in Table 1, most functions were labeled as “Useful”. Weused a one-tailed binomial test to statistically compare the percentages of each

row. The results are also displayed on Table 1, the ‘Useful’ label occurrence wasstatically higher in all cases than the second place label, except in the third row(Filtering Module) where both the “Very Useful” label and the “Useful” labelhave statistically more occurrences than “Somewhat Useful” (third place). Themost useful functions, according the answers, were the Expertise Finding engineand the Filtering engine.

Regarding the Expertise Finding, we asked volunteers about what they lookfor in answers from their Facebook friends. Figure 6 presents a summary of theseanswers.

Fig. 6: The results about what users expect from answers from their Facebook friends.

“Truth” (27%) was the most desired characteristic followed by “Detail”(21%), which means that the utility score function should prioritize the Ex-pertise over other subjective criteria like friendship, trust, bond, availability,distance, etc. We obtained an unexpected result, because “Personalization” wasthe less desirable characteristic (2%), while in [13] many appreciated that theirprivate SN was familiar with their additional context, such as knowledge of theirlocation, family situation, or other preferences. Impartiality did not receive manyvotes either (4%).

Later, we asked if they believed that certain questions were implicitly directedto people in a demographic group. We used a template question like “Do youagree that some questions can only be answered by a certain [characteristic]?”followed by each Filter option. This question aimed to evaluate the practicalutility of the Filtering engine and their results are summarized in Figure 7.

In general, all the Filters were considered useful by most of volunteers, exceptthe gender filter, which was a polemic subject. We believe that this rejection wasdue to the sexist aspect of our question. Unfortunately, we do not have informa-tion about gender from our volunteers; however, based on the email address, wethink that 15% of them are women, 24% are men, and 61% are unknown. Thus,38% of “women” (by our standards) considered the Gender Filter useless, while

Fig. 7: The results about the Filtering engine utility.

45% of “man” thought it was useful. When we observe the difference betweenthe filter acceptance percentage by men and women, we realize that the Genderfilter has the greatest difference (14%, while others did not exceed 5%). Thismay be absurd, but men and women may have understood that they were notable to answer questions made by the other gender and rejected the filter by thisreason. But this is just a guess; we could not confirm this without individuallyinterviewing each respondent. The fact is that the Gender filter was not wellreceived by our audience.

6 Conclusion and Future Work

In this work, we presented the Social Query app to assist users to search forinformation on SNs. While most part of previous work focused on the ExpertiseFinding engine, we propose a tool to help the users through several steps of thesocial search process. First, our solution helps the users to rephrase the question,enhancing its probability of being answered. Second, the app offers three differentapproaches to finding experts. Last, there is an option to filter the expert findingsearch to a certain group with the same demographic characteristics as the users(age or gender, for instance).

To evaluate our proposal, we ran a questionnaire, which was answered by250 Facebook users. Through the questionnaire, these users could give theirimpressions about the functional aspect of the Social Query app. The results wereexcellent. The main functions (Expertise Finding mechanism, Filtering engineand Rephrase engine) of the app, in average, were considered at least useful bymore than 40% of users. In addition, we obtained great feedback that allows usto think in improvements to our proposal.

As Future Work, we are planning the following improvements: (1) use ofother Expertise Finding models, including those which consider semantics; (2)improve the Question Analyzer, besides suggesting changes in problem speci-fication, automatically applying some or all of these changes; (3) improve the

Filtering use to specify the input; (4) allow users to maintain a list of contacts;(5) allow users to maintain lists of friends; (6) considering additionally the users’reputation, based on previous; and (7) make friends of friends available as expertcandidates.

7 Acknowledgments

We want to thank the people who answered our questionnaire.

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